Regression modelling
Bradley McKenzie
June 2025
Libraries.
## Loading required package: Matrix## Loaded glmnet 4.1-8##
## Please cite as:## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.## R package version 5.2.3. https://CRAN.R-project.org/package=stargazer##
## Attaching package: 'mice'## The following object is masked from 'package:stats':
##
## filter## The following objects are masked from 'package:base':
##
## cbind, rbind## Welcome to emmeans.
## Caution: You lose important information if you filter this package's results.
## See '? untidy'Purpose
This document will run through modelling to test the change in the relationship between trust in the UK Parliament and trust in the EU parliament within the UK.
We will run our other scripts to generate our clean GB dataset for modelling. This comes from these scripts.
Additionally, the analysis includes the
“Difference-in-difference.Rmd” script. But this is not required to be
run for this analysis. Our main dataframe to use for analysis is
“gb_clean” which includes: - GB only information from the ESS survey -
Information from Round 5 to 11 of the survey. - Cleaned variables and
recoded demographic levels from the
Multivariate analysis and var cleaning.Rmd script
Note: This script is computationally expensive. On the tested device it took 10+minutes to run. The slowest section of code is the multiple imputation section. In the model, update from m=5 to m=2 to see faster results (although this is not recommended for final outputs).
Modelling approach.
This script will include multiple models to find the best model. The approaches will include:
- Creating the target variable for relationship between trust_europ
(EU Parl) and trust_parliament (local GB Parl)
- test with a difference value (trust_europ - trust_parliament)
- test with a ratio (trust_europ / trust_parliament)
Based on some literature, a raw difference value is preferred because it is easier to interpret. However some recommend a ratio, so we will test both to check the distribution and conduct basic
- Run multiple regression models for:
- null model
- full model
- regularized model (with lasso)
- with and without interaction terms for time.
All models are weighted with ‘anweight’. Because of this, modelling is conducted through surveyglm functions within the glm package.
Notes on the analysis:
- We treat the treating target variable as continuous (family = gaussian) in all models. If time allows, the users should test additional models, such as modelling the target at ordinal. Or creating a categorical variable for “higher EU trust”, “higher UK trust” and “equal” trust, and model “higher EU trust” as a binary target.
- Additionally, in Round 11 of ESS, the variable vteubcmb was introduced - Would vote for [country] to become member of European Union or remain outside https://ess.sikt.no/en/datafile/242aaa39-3bbb-40f5-98bf-bfb1ce53d8ef?tab=1&elems=01e8a05c-aad0-41f2-8e4c-6d0f98dee44b_1. This should be investigated further in the study.
Assumptions:
- In the interaction models, time is treated as a factor. It is not modelled as numeric which could show a linear relationship. Due to the short time period (4 periods) and political volatility, we do not assume the trends are linear. The factor outputs are shown.
- Additionally, the normality assumption on the target assumption is somewhat loose, this is suitable for the analysis of this study, but the suggested models above should be made if you want to publish these findings.
Prepare our GB dataset
Limit the data to only round 8 onwards. As we saw in the DiD the effects are not significant, we are focussed on just the interaction between these 2 variables and how euroscepticism has changed in the UK since the Referendum.
gb_clean_regress |> group_by(essround) |> summarise(total_na = sum(is.na(ethnic_minority)),
valid = sum(!is.na(ethnic_minority)))Test and plot the target variables - raw diff and ratio
Create the 2 variables.
target_diff = trust_europ - trust_parliament target_ratio = trust_europ / trust_europ (but incl +0.1 for each trust value if == 0 to avoid errors and negation. )
gb_clean_regress <- gb_clean_regress |>
mutate(target_diff = trust_europ - trust_parliament,
trust_europ2 = ifelse(trust_europ == 0, 0.1, trust_europ),
trust_parliament2 = ifelse(trust_parliament == 0, 0.1, trust_parliament),
target_ratio = trust_europ2 / trust_parliament2,
target_ratiolog = log(target_ratio))
gb_clean_regress |> select(target_diff, target_ratio, target_ratiolog) |> plot_histogram()
## target_diff target_ratio target_ratiolog
## Min. :-10.0000 Min. : 0.0100 Min. :-4.6052
## 1st Qu.: -2.0000 1st Qu.: 0.5556 1st Qu.:-0.5878
## Median : 0.0000 Median : 1.0000 Median : 0.0000
## Mean : -0.6205 Mean : 2.5701 Mean :-0.3404
## 3rd Qu.: 1.0000 3rd Qu.: 1.1429 3rd Qu.: 0.1335
## Max. : 10.0000 Max. :100.0000 Max. : 4.6052
## NA's :529 NA's :529 NA's :529And plot more nicely for the report.
First, check by wave distribution appears to be flattening and shifting towards the EU, but is difficult to see distribution here due to different sample sizes in each wave.
gb_clean_regress |>
group_by(essround, target_diff) |>
summarise(obs = n()) |>
ggplot(aes(x=target_diff, y = obs, group = essround, colour = essround))+
geom_line()## `summarise()` has grouped output by 'essround'. You can override using the
## `.groups` argument.## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_line()`).
Ratio transformation plot - for report
Plot the non-transformed data next to the transformed data for the report:
gb_clean_regress |>
group_by(essround, target_ratio) |>
summarise(obs = n()) |>
ggplot(aes(x=target_ratio, y = obs, group = essround, colour = essround))+
geom_line()## `summarise()` has grouped output by 'essround'. You can override using the
## `.groups` argument.## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Create density plots to compare
## raw ratio
p1 <- ggplot(gb_clean_regress, aes(x = target_ratio)) +
geom_density(fill = "skyblue", alpha = 0.7, adjust = 5) +
labs(
#title = "Distribution of unadjusted Trust Ratio",
x = "Raw trust ratio (Trust EU / Trust UK)",
y = "Density") +
theme_minimal()+
lims(x=c(0,50), y = c(0,0.7))
## log target
p2 <- ggplot(gb_clean_regress, aes(x = target_ratiolog)) +
geom_density(fill = "lightcoral", alpha = 0.7, adjust = 5) +
labs(
#title = "Distribution of Log-Transformed Trust Ratio",
x = "Log transformed ratio",
y = NULL
) +
theme_minimal()+
lims(y = c(0,0.7))
# theme(
# axis.title.y = element_blank(), # Explicitly hide the axis title as well
# axis.text.y = element_blank(), # Optionally hide axis text/ticks if you want it even closer and don't need the scale on the right
# # axis.ticks.y = element_blank(),
# plot.margin = unit(c(0.2, 0.2, 0.2, 0.0), "cm") # Reduce left margin of p2
# )
# display:
p1 + p2## Warning: Removed 588 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning: Removed 529 rows containing non-finite outside the scale range
## (`stat_density()`).
Check correlations for interpretations:
# plot correlations:
cor_matrix <- cor(gb_clean_regress |>
select(target_diff, target_ratio, target_ratiolog),
use = "pairwise.complete.obs")
corrplot(cor_matrix, method = "color", type = "upper",
addCoef.col = "black", tl.col = "black",
col = colorRampPalette(c("blue", "white", "red"))(200))
gb_clean_regress |> select(target_diff, target_ratiolog) |> drop_na() |> sjPlot::plot_scatter(target_diff, target_ratiolog)
We see that the raw ratio is least useful. We decide between either the log of the ratio or the straight difference. The log ratio would allow us to model a continuous target variable but is less interpretable. If we are only concerned with the direction of the trend however, this is ok.
Modelling crude measures, pre-imputation
Modelling of EU trust
Using the already run ESS data and selecting a subset, we test our drivers of trust in european parliament as the target to understand the data first.
## can centre data on year.
# my_survey_data <- my_survey_data %>%
# mutate(year_centered = year - min(year)) # Or just use 'year' directly
# Redefine survey design with the modified data
survey_design_rgrss <- svydesign(ids = ~1,
weights = ~anweight,
data = gb_clean_regress)
# Fit the regression model
null_model <- svyglm(
trust_europ ~ 1,
design = survey_design_rgrss,
family = gaussian()
)
base_time_model <- svyglm(
trust_europ ~ essround,
design = survey_design_rgrss,
family = gaussian()
)
summary(base_time_model)##
## Call:
## svyglm(formula = trust_europ ~ essround, design = survey_design_rgrss,
## family = gaussian())
##
## Survey design:
## svydesign(ids = ~1, weights = ~anweight, data = gb_clean_regress)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.77635 0.06624 57.007 <2e-16 ***
## essround9 -0.16828 0.09268 -1.816 0.0695 .
## essround10 0.25212 0.11210 2.249 0.0245 *
## essround11 0.24185 0.10128 2.388 0.0170 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 6.365428)
##
## Number of Fisher Scoring iterations: 2## full model.
full_model <- svyglm(
trust_europ ~ essround + gender + age_rec + educ_level + income_group + income_stress + ethnic_minority + area_type + health_disability + left_right + polintr_binary + life_sat + econ_sat + immig_support + daily_netuse + country_attach + post_polonline,
design = survey_design_rgrss,
family = gaussian()
)
summary(full_model)##
## Call:
## svyglm(formula = trust_europ ~ essround + gender + age_rec +
## educ_level + income_group + income_stress + ethnic_minority +
## area_type + health_disability + left_right + polintr_binary +
## life_sat + econ_sat + immig_support + daily_netuse + country_attach +
## post_polonline, design = survey_design_rgrss, family = gaussian())
##
## Survey design:
## svydesign(ids = ~1, weights = ~anweight, data = gb_clean_regress)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 7.09419 0.43044 16.481
## essround9 -0.14170 0.08773 -1.615
## genderFemale 0.38835 0.08826 4.400
## age_rec20-29 -1.13725 0.32717 -3.476
## age_rec30-44 -1.46756 0.31356 -4.680
## age_rec45-64 -1.74488 0.31209 -5.591
## age_rec>=65 -2.18986 0.31991 -6.845
## educ_levelUpper secondary -0.18076 0.13387 -1.350
## educ_levelTertiary 0.30380 0.12749 2.383
## income_groupLow (Decile <4) 0.10050 0.13981 0.719
## income_groupModerate (Decile 4-7) 0.02237 0.10999 0.203
## income_stressCoping on present income -0.09977 0.10146 -0.983
## income_stressDifficult on present income -0.04155 0.18730 -0.222
## income_stressVery difficult on present income 0.02104 0.37228 0.057
## ethnic_minorityNo -0.30337 0.19292 -1.573
## area_typeSuburbs or outskirts of big city -0.28002 0.20034 -1.398
## area_typeTown or small city -0.39759 0.19092 -2.082
## area_typeCountry -0.48633 0.19591 -2.482
## health_disabilityYes -0.08953 0.11089 -0.807
## left_rightModerate (4-6) -0.34160 0.12273 -2.783
## left_rightRight (7-10) -0.58499 0.15804 -3.702
## polintr_binaryYes 0.27166 0.10141 2.679
## life_satModerate -0.47511 0.12654 -3.755
## life_satLow -0.79909 0.22391 -3.569
## econ_satModerate -0.22675 0.12032 -1.885
## econ_satLow -0.87255 0.14814 -5.890
## immig_supportModerate -1.09592 0.10879 -10.073
## immig_supportLow -2.13785 0.13418 -15.932
## daily_netuseYes 0.03843 0.12026 0.320
## country_attachModerate 0.07808 0.10862 0.719
## country_attachLow -0.10343 0.16932 -0.611
## post_polonlineYes 0.12263 0.10735 1.142
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## essround9 0.106368
## genderFemale 1.12e-05 ***
## age_rec20-29 0.000516 ***
## age_rec30-44 2.99e-06 ***
## age_rec45-64 2.46e-08 ***
## age_rec>=65 9.24e-12 ***
## educ_levelUpper secondary 0.177027
## educ_levelTertiary 0.017237 *
## income_groupLow (Decile <4) 0.472287
## income_groupModerate (Decile 4-7) 0.838820
## income_stressCoping on present income 0.325502
## income_stressDifficult on present income 0.824467
## income_stressVery difficult on present income 0.954934
## ethnic_minorityNo 0.115930
## area_typeSuburbs or outskirts of big city 0.162290
## area_typeTown or small city 0.037383 *
## area_typeCountry 0.013102 *
## health_disabilityYes 0.419545
## left_rightModerate (4-6) 0.005415 **
## left_rightRight (7-10) 0.000218 ***
## polintr_binaryYes 0.007425 **
## life_satModerate 0.000177 ***
## life_satLow 0.000364 ***
## econ_satModerate 0.059579 .
## econ_satLow 4.30e-09 ***
## immig_supportModerate < 2e-16 ***
## immig_supportLow < 2e-16 ***
## daily_netuseYes 0.749314
## country_attachModerate 0.472267
## country_attachLow 0.541337
## post_polonlineYes 0.253389
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 3.57081)
##
## Number of Fisher Scoring iterations: 2Print results with stargazer for each model:
stargazer(null_model, base_time_model, full_model,
type = "text", # "text" for console, "html" for web, "latex" for LaTeX
title = "Comparison of Regression Models on Trust in Europe",
dep.var.labels = "Trust in Europe (0-10 Scale)",
column.labels = c("Null model", "Time only", "Full covariate model"),
# out = "models_comparison.html", # To save to an HTML file
align = TRUE, # Align decimals
no.space = TRUE, # Reduce vertical space
# add.lines = list(c("Survey-Weighted", "Yes", "Yes", "Yes")), # Example of adding custom line
star.cutoffs = c(0.05, 0.01, 0.001), # Define significance levels
notes = "Standard errors in parentheses. * p < 0.05, ** p < 0.01, *** p < 0.001",
notes.align = "l"
)##
## Comparison of Regression Models on Trust in Europe
## ======================================================================================================================
## Dependent variable:
## ------------------------------------------------------------------------
## Trust in Europe (0-10 Scale)
## Null model Time only Full covariate model
## (1) (2) (3)
## ----------------------------------------------------------------------------------------------------------------------
## essround9 -0.168 -0.142
## (0.093) (0.088)
## essround10 0.252*
## (0.112)
## essround11 0.242*
## (0.101)
## genderFemale 0.388***
## (0.088)
## age_rec20-29 -1.137***
## (0.327)
## age_rec30-44 -1.468***
## (0.314)
## age_rec45-64 -1.745***
## (0.312)
## age_rec> =65 -2.190***
## (0.320)
## educ_levelUpper secondary -0.181
## (0.134)
## educ_levelTertiary 0.304*
## (0.127)
## income_groupLow (Decile < 4) 0.101
## (0.140)
## income_groupModerate (Decile 4-7) 0.022
## (0.110)
## income_stressCoping on present income -0.100
## (0.101)
## income_stressDifficult on present income -0.042
## (0.187)
## income_stressVery difficult on present income 0.021
## (0.372)
## ethnic_minorityNo -0.303
## (0.193)
## area_typeSuburbs or outskirts of big city -0.280
## (0.200)
## area_typeTown or small city -0.398*
## (0.191)
## area_typeCountry -0.486*
## (0.196)
## health_disabilityYes -0.090
## (0.111)
## left_rightModerate (4-6) -0.342**
## (0.123)
## left_rightRight (7-10) -0.585***
## (0.158)
## polintr_binaryYes 0.272**
## (0.101)
## life_satModerate -0.475***
## (0.127)
## life_satLow -0.799***
## (0.224)
## econ_satModerate -0.227
## (0.120)
## econ_satLow -0.873***
## (0.148)
## immig_supportModerate -1.096***
## (0.109)
## immig_supportLow -2.138***
## (0.134)
## daily_netuseYes 0.038
## (0.120)
## country_attachModerate 0.078
## (0.109)
## country_attachLow -0.103
## (0.169)
## post_polonlineYes 0.123
## (0.107)
## Constant 3.855*** 3.776*** 7.094***
## (0.038) (0.066) (0.430)
## ----------------------------------------------------------------------------------------------------------------------
## Observations 6,494 6,494 3,001
## Log Likelihood -15,815.170 -15,799.270 -6,687.708
## Akaike Inf. Crit. 31,632.330 31,606.550 13,439.420
## ======================================================================================================================
## Note: *p<0.05; **p<0.01; ***p<0.001
## Standard errors in parentheses. * p < 0.05, ** p < 0.01, *** p < 0.001Model raw target difference
Model raw trust_diff as continuous variable:
## can centre data on year.
# my_survey_data <- my_survey_data %>%
# mutate(year_centered = year - min(year)) # Or just use 'year' directly
# Redefine survey design with the modified data
survey_design_rgrss <- svydesign(ids = ~1,
weights = ~anweight,
data = gb_clean_regress)
# Fit the regression model
null_model <- svyglm(
target_diff ~ 1,
design = survey_design_rgrss,
family = gaussian()
)
base_time_model <- svyglm(
target_diff ~ essround,
design = survey_design_rgrss,
family = gaussian()
)
summary(base_time_model)##
## Call:
## svyglm(formula = target_diff ~ essround, design = survey_design_rgrss,
## family = gaussian())
##
## Survey design:
## svydesign(ids = ~1, weights = ~anweight, data = gb_clean_regress)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.93222 0.06377 -14.618 < 2e-16 ***
## essround9 0.17416 0.08823 1.974 0.0484 *
## essround10 0.75109 0.11271 6.664 2.89e-11 ***
## essround11 1.11320 0.09820 11.336 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 6.286111)
##
## Number of Fisher Scoring iterations: 2## full model.
full_model <- svyglm(
target_diff ~ essround + gender + age_rec + educ_level + income_group + income_stress + ethnic_minority + area_type + health_disability + left_right + polintr_binary + life_sat + econ_sat + immig_support + daily_netuse + country_attach + post_polonline,
design = survey_design_rgrss,
family = gaussian()
)
summary(full_model)##
## Call:
## svyglm(formula = target_diff ~ essround + gender + age_rec +
## educ_level + income_group + income_stress + ethnic_minority +
## area_type + health_disability + left_right + polintr_binary +
## life_sat + econ_sat + immig_support + daily_netuse + country_attach +
## post_polonline, design = survey_design_rgrss, family = gaussian())
##
## Survey design:
## svydesign(ids = ~1, weights = ~anweight, data = gb_clean_regress)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.6902650 0.4537530 1.521
## essround9 0.1508211 0.0931255 1.620
## genderFemale 0.3918700 0.0925982 4.232
## age_rec20-29 -0.8531818 0.3231821 -2.640
## age_rec30-44 -1.0910722 0.3090396 -3.531
## age_rec45-64 -1.3114445 0.3146176 -4.168
## age_rec>=65 -1.7660456 0.3250824 -5.433
## educ_levelUpper secondary -0.2101135 0.1437992 -1.461
## educ_levelTertiary -0.1293235 0.1315085 -0.983
## income_groupLow (Decile <4) 0.2063506 0.1387474 1.487
## income_groupModerate (Decile 4-7) -0.0113923 0.1162882 -0.098
## income_stressCoping on present income -0.0215513 0.1024178 -0.210
## income_stressDifficult on present income 0.0042452 0.1896064 0.022
## income_stressVery difficult on present income 0.1079275 0.4481237 0.241
## ethnic_minorityNo 0.0024399 0.1929241 0.013
## area_typeSuburbs or outskirts of big city -0.4394459 0.2431373 -1.807
## area_typeTown or small city -0.1937450 0.2282504 -0.849
## area_typeCountry -0.2178645 0.2350089 -0.927
## health_disabilityYes 0.0005475 0.1167665 0.005
## left_rightModerate (4-6) -0.5498047 0.1308053 -4.203
## left_rightRight (7-10) -1.4530654 0.1644664 -8.835
## polintr_binaryYes -0.0720140 0.1053385 -0.684
## life_satModerate -0.1957575 0.1252201 -1.563
## life_satLow -0.1160761 0.3033476 -0.383
## econ_satModerate 0.4265293 0.1219333 3.498
## econ_satLow 0.9891906 0.1587342 6.232
## immig_supportModerate -0.5219317 0.1126850 -4.632
## immig_supportLow -0.8251272 0.1547999 -5.330
## daily_netuseYes -0.0555635 0.1176616 -0.472
## country_attachModerate 0.5101692 0.1149343 4.439
## country_attachLow 1.1438682 0.1876968 6.094
## post_polonlineYes 0.2605311 0.1128857 2.308
## Pr(>|t|)
## (Intercept) 0.128308
## essround9 0.105436
## genderFemale 2.39e-05 ***
## age_rec20-29 0.008336 **
## age_rec30-44 0.000421 ***
## age_rec45-64 3.16e-05 ***
## age_rec>=65 6.00e-08 ***
## educ_levelUpper secondary 0.144078
## educ_levelTertiary 0.325498
## income_groupLow (Decile <4) 0.137058
## income_groupModerate (Decile 4-7) 0.921966
## income_stressCoping on present income 0.833351
## income_stressDifficult on present income 0.982139
## income_stressVery difficult on present income 0.809693
## ethnic_minorityNo 0.989910
## area_typeSuburbs or outskirts of big city 0.070802 .
## area_typeTown or small city 0.396046
## area_typeCountry 0.353977
## health_disabilityYes 0.996259
## left_rightModerate (4-6) 2.71e-05 ***
## left_rightRight (7-10) < 2e-16 ***
## polintr_binaryYes 0.494254
## life_satModerate 0.118087
## life_satLow 0.702006
## econ_satModerate 0.000476 ***
## econ_satLow 5.27e-10 ***
## immig_supportModerate 3.78e-06 ***
## immig_supportLow 1.05e-07 ***
## daily_netuseYes 0.636796
## country_attachModerate 9.38e-06 ***
## country_attachLow 1.24e-09 ***
## post_polonlineYes 0.021072 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 3.773593)
##
## Number of Fisher Scoring iterations: 2Print results with stargazer for each model:
stargazer(null_model, base_time_model, full_model,
type = "text", # "text" for console, "html" for web, "latex" for LaTeX
title = "Comparison of Regression Models on Trust in Europe",
dep.var.labels = "Trust in Europe (0-10 Scale)",
column.labels = c("Null model", "Time only", "Full covariate model"),
# out = "models_comparison.html", # To save to an HTML file
align = TRUE, # Align decimals
no.space = TRUE, # Reduce vertical space
# add.lines = list(c("Survey-Weighted", "Yes", "Yes", "Yes")), # Example of adding custom line
star.cutoffs = c(0.05, 0.01, 0.001), # Define significance levels
notes = "Standard errors in parentheses. * p < 0.05, ** p < 0.01, *** p < 0.001",
notes.align = "l"
)##
## Comparison of Regression Models on Trust in Europe
## ======================================================================================================================
## Dependent variable:
## ------------------------------------------------------------------------
## Trust in Europe (0-10 Scale)
## Null model Time only Full covariate model
## (1) (2) (3)
## ----------------------------------------------------------------------------------------------------------------------
## essround9 0.174* 0.151
## (0.088) (0.093)
## essround10 0.751***
## (0.113)
## essround11 1.113***
## (0.098)
## genderFemale 0.392***
## (0.093)
## age_rec20-29 -0.853**
## (0.323)
## age_rec30-44 -1.091***
## (0.309)
## age_rec45-64 -1.311***
## (0.315)
## age_rec> =65 -1.766***
## (0.325)
## educ_levelUpper secondary -0.210
## (0.144)
## educ_levelTertiary -0.129
## (0.132)
## income_groupLow (Decile < 4) 0.206
## (0.139)
## income_groupModerate (Decile 4-7) -0.011
## (0.116)
## income_stressCoping on present income -0.022
## (0.102)
## income_stressDifficult on present income 0.004
## (0.190)
## income_stressVery difficult on present income 0.108
## (0.448)
## ethnic_minorityNo 0.002
## (0.193)
## area_typeSuburbs or outskirts of big city -0.439
## (0.243)
## area_typeTown or small city -0.194
## (0.228)
## area_typeCountry -0.218
## (0.235)
## health_disabilityYes 0.001
## (0.117)
## left_rightModerate (4-6) -0.550***
## (0.131)
## left_rightRight (7-10) -1.453***
## (0.164)
## polintr_binaryYes -0.072
## (0.105)
## life_satModerate -0.196
## (0.125)
## life_satLow -0.116
## (0.303)
## econ_satModerate 0.427***
## (0.122)
## econ_satLow 0.989***
## (0.159)
## immig_supportModerate -0.522***
## (0.113)
## immig_supportLow -0.825***
## (0.155)
## daily_netuseYes -0.056
## (0.118)
## country_attachModerate 0.510***
## (0.115)
## country_attachLow 1.144***
## (0.188)
## post_polonlineYes 0.261*
## (0.113)
## Constant -0.432*** -0.932*** 0.690
## (0.038) (0.064) (0.454)
## ----------------------------------------------------------------------------------------------------------------------
## Observations 6,467 6,467 2,994
## Log Likelihood -15,793.060 -15,694.140 -6,755.385
## Akaike Inf. Crit. 31,588.120 31,396.280 13,574.770
## ======================================================================================================================
## Note: *p<0.05; **p<0.01; ***p<0.001
## Standard errors in parentheses. * p < 0.05, ** p < 0.01, *** p < 0.001Model ratio log target
Model the ratio of trustEU / trustUK parliaments that has been logged as continuous variable:
# Redefine survey design with the modified data
survey_design_rgrss <- svydesign(ids = ~1,
weights = ~anweight,
data = gb_clean_regress)
# Fit the regression model
null_model <- svyglm(
target_ratiolog ~ 1,
design = survey_design_rgrss,
family = gaussian()
)
base_time_model <- svyglm(
target_ratiolog ~ essround,
design = survey_design_rgrss,
family = gaussian()
)
summary(base_time_model)##
## Call:
## svyglm(formula = target_ratiolog ~ essround, design = survey_design_rgrss,
## family = gaussian())
##
## Survey design:
## svydesign(ids = ~1, weights = ~anweight, data = gb_clean_regress)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.44043 0.03399 -12.957 < 2e-16 ***
## essround9 0.04565 0.04851 0.941 0.347
## essround10 0.28641 0.05793 4.944 7.83e-07 ***
## essround11 0.43192 0.05342 8.086 7.30e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.935723)
##
## Number of Fisher Scoring iterations: 2## full model.
full_model <- svyglm(
target_ratiolog ~ essround + gender + age_rec + educ_level + income_group + income_stress + ethnic_minority + area_type + health_disability + left_right + polintr_binary + life_sat + econ_sat + immig_support + daily_netuse + country_attach + post_polonline,
design = survey_design_rgrss,
family = gaussian()
)
summary(full_model)##
## Call:
## svyglm(formula = target_ratiolog ~ essround + gender + age_rec +
## educ_level + income_group + income_stress + ethnic_minority +
## area_type + health_disability + left_right + polintr_binary +
## life_sat + econ_sat + immig_support + daily_netuse + country_attach +
## post_polonline, design = survey_design_rgrss, family = gaussian())
##
## Survey design:
## svydesign(ids = ~1, weights = ~anweight, data = gb_clean_regress)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.425624 0.204829 2.078
## essround9 -0.010624 0.051453 -0.206
## genderFemale 0.218137 0.050867 4.288
## age_rec20-29 -0.285202 0.148087 -1.926
## age_rec30-44 -0.530478 0.142260 -3.729
## age_rec45-64 -0.579471 0.148175 -3.911
## age_rec>=65 -0.816105 0.153836 -5.305
## educ_levelUpper secondary -0.108313 0.087883 -1.232
## educ_levelTertiary -0.009164 0.075564 -0.121
## income_groupLow (Decile <4) 0.063548 0.079814 0.796
## income_groupModerate (Decile 4-7) 0.006236 0.061961 0.101
## income_stressCoping on present income -0.021094 0.055939 -0.377
## income_stressDifficult on present income 0.040194 0.098680 0.407
## income_stressVery difficult on present income -0.108022 0.241371 -0.448
## ethnic_minorityNo -0.072609 0.097336 -0.746
## area_typeSuburbs or outskirts of big city -0.118792 0.123952 -0.958
## area_typeTown or small city -0.032516 0.117008 -0.278
## area_typeCountry -0.093922 0.120799 -0.778
## health_disabilityYes -0.049367 0.068796 -0.718
## left_rightModerate (4-6) -0.183866 0.068736 -2.675
## left_rightRight (7-10) -0.576173 0.089663 -6.426
## polintr_binaryYes -0.091244 0.059993 -1.521
## life_satModerate -0.194384 0.074549 -2.607
## life_satLow -0.084991 0.197226 -0.431
## econ_satModerate 0.075065 0.058839 1.276
## econ_satLow 0.367760 0.084362 4.359
## immig_supportModerate -0.296362 0.060046 -4.936
## immig_supportLow -0.642280 0.092332 -6.956
## daily_netuseYes 0.102294 0.069765 1.466
## country_attachModerate 0.154068 0.064715 2.381
## country_attachLow 0.381632 0.107693 3.544
## post_polonlineYes 0.085440 0.057758 1.479
## Pr(>|t|)
## (Intercept) 0.037800 *
## essround9 0.836431
## genderFemale 1.86e-05 ***
## age_rec20-29 0.054211 .
## age_rec30-44 0.000196 ***
## age_rec45-64 9.41e-05 ***
## age_rec>=65 1.21e-07 ***
## educ_levelUpper secondary 0.217869
## educ_levelTertiary 0.903482
## income_groupLow (Decile <4) 0.425974
## income_groupModerate (Decile 4-7) 0.919845
## income_stressCoping on present income 0.706139
## income_stressDifficult on present income 0.683806
## income_stressVery difficult on present income 0.654522
## ethnic_minorityNo 0.455746
## area_typeSuburbs or outskirts of big city 0.337954
## area_typeTown or small city 0.781113
## area_typeCountry 0.436921
## health_disabilityYes 0.473068
## left_rightModerate (4-6) 0.007515 **
## left_rightRight (7-10) 1.52e-10 ***
## polintr_binaryYes 0.128390
## life_satModerate 0.009167 **
## life_satLow 0.666549
## econ_satModerate 0.202139
## econ_satLow 1.35e-05 ***
## immig_supportModerate 8.43e-07 ***
## immig_supportLow 4.28e-12 ***
## daily_netuseYes 0.142682
## country_attachModerate 0.017342 *
## country_attachLow 0.000401 ***
## post_polonlineYes 0.139170
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.203729)
##
## Number of Fisher Scoring iterations: 2Print results with stargazer for each model:
stargazer(null_model, base_time_model, full_model,
type = "text", # "text" for console, "html" for web, "latex" for LaTeX
title = "Comparison of Regression Models on Trust in Europe",
dep.var.labels = "Trust in Europe (0-10 Scale)",
column.labels = c("Null model", "Time only", "Full covariate model"),
# out = "models_comparison.html", # To save to an HTML file
align = TRUE, # Align decimals
no.space = TRUE, # Reduce vertical space
# add.lines = list(c("Survey-Weighted", "Yes", "Yes", "Yes")), # Example of adding custom line
star.cutoffs = c(0.05, 0.01, 0.001), # Define significance levels
notes = "Standard errors in parentheses. * p < 0.05, ** p < 0.01, *** p < 0.001",
notes.align = "l"
)##
## Comparison of Regression Models on Trust in Europe
## ======================================================================================================================
## Dependent variable:
## ------------------------------------------------------------------------
## Trust in Europe (0-10 Scale)
## Null model Time only Full covariate model
## (1) (2) (3)
## ----------------------------------------------------------------------------------------------------------------------
## essround9 0.046 -0.011
## (0.049) (0.051)
## essround10 0.286***
## (0.058)
## essround11 0.432***
## (0.053)
## genderFemale 0.218***
## (0.051)
## age_rec20-29 -0.285
## (0.148)
## age_rec30-44 -0.530***
## (0.142)
## age_rec45-64 -0.579***
## (0.148)
## age_rec> =65 -0.816***
## (0.154)
## educ_levelUpper secondary -0.108
## (0.088)
## educ_levelTertiary -0.009
## (0.076)
## income_groupLow (Decile < 4) 0.064
## (0.080)
## income_groupModerate (Decile 4-7) 0.006
## (0.062)
## income_stressCoping on present income -0.021
## (0.056)
## income_stressDifficult on present income 0.040
## (0.099)
## income_stressVery difficult on present income -0.108
## (0.241)
## ethnic_minorityNo -0.073
## (0.097)
## area_typeSuburbs or outskirts of big city -0.119
## (0.124)
## area_typeTown or small city -0.033
## (0.117)
## area_typeCountry -0.094
## (0.121)
## health_disabilityYes -0.049
## (0.069)
## left_rightModerate (4-6) -0.184**
## (0.069)
## left_rightRight (7-10) -0.576***
## (0.090)
## polintr_binaryYes -0.091
## (0.060)
## life_satModerate -0.194**
## (0.075)
## life_satLow -0.085
## (0.197)
## econ_satModerate 0.075
## (0.059)
## econ_satLow 0.368***
## (0.084)
## immig_supportModerate -0.296***
## (0.060)
## immig_supportLow -0.642***
## (0.092)
## daily_netuseYes 0.102
## (0.070)
## country_attachModerate 0.154*
## (0.065)
## country_attachLow 0.382***
## (0.108)
## post_polonlineYes 0.085
## (0.058)
## Constant -0.253*** -0.440*** 0.426*
## (0.020) (0.034) (0.205)
## ----------------------------------------------------------------------------------------------------------------------
## Observations 6,467 6,467 2,994
## Log Likelihood -11,936.310 -11,885.520 -5,044.908
## Akaike Inf. Crit. 23,874.620 23,779.040 10,153.820
## ======================================================================================================================
## Note: *p<0.05; **p<0.01; ***p<0.001
## Standard errors in parentheses. * p < 0.05, ** p < 0.01, *** p < 0.001# print results that are significant
tibble(broom::tidy(full_model) |>
filter(p.value<0.05)) |>
arrange(desc(estimate))We see that the full model misses many observations due to missing values. We need to adress this before finalising the model.
But the full model appears significantly better with it’s lower AIC. Also we see that many of the variables have a statistically signficant relationship with trust in EU Parliament: More likely to trust EU Parliament include: - Feeling less attached to the UK - Low economic satisfaction - Being Female - Having more left leaning views
While more lower trust in EU Parliament include: - Being older - Having less support for immigrants
Imputing missing values:
Here we check the distribution of missing values and try to apply imputation for those variables we can complete.
# we remove ethnic minority due to missing R10 and R11 values.
gb_clean_regress <- gb_clean_regress |>
select(-ethnic_minority) Standardise all variables to factors:
# and make polintr binary for the imputation
gb_clean_regress <- gb_clean_regress |>
mutate(polintr_binary = as_factor(polintr_binary),
cntry = as_factor(cntry),
trust_europ = as.numeric(trust_europ),
trust_parliament = as.numeric(trust_parliament),
gender = as_factor(gender),
income_stress = as_factor(income_stress),
area_type = as_factor(area_type),
region = as_factor(region))
str(gb_clean_regress)## tibble [6,996 × 26] (S3: tbl_df/tbl/data.frame)
## $ essround : Factor w/ 4 levels "8","9","10","11": 1 1 1 1 1 1 1 1 1 1 ...
## $ cntry : Factor w/ 40 levels "Albania","Austria",..: 14 14 14 14 14 14 14 14 14 14 ...
## ..- attr(*, "label")= chr "Country"
## $ anweight : num [1:6996] 2.63 2.28 2.63 2.11 2.1 ...
## $ trust_europ : num [1:6996] 5 0 5 0 6 NA 2 3 7 2 ...
## $ trust_parliament : num [1:6996] 8 2 5 0 2 9 3 5 7 8 ...
## $ gender : Factor w/ 3 levels "Male","Female",..: 1 1 2 2 1 1 2 2 1 1 ...
## ..- attr(*, "label")= chr "Gender"
## $ age_rec : Factor w/ 5 levels "<20","20-29",..: 4 4 4 4 2 3 3 2 3 5 ...
## $ educ_level : Factor w/ 3 levels "Lower secondary",..: 2 3 2 2 1 3 3 2 3 1 ...
## $ income_group : Factor w/ 3 levels "High (Decile 8+)",..: 3 1 2 3 2 NA 3 3 1 2 ...
## $ income_stress : Factor w/ 7 levels "Living comfortably on present income",..: 2 1 1 1 2 1 2 2 2 2 ...
## ..- attr(*, "label")= chr "Feeling about household's income nowadays"
## $ area_type : Factor w/ 7 levels "A big city","Suburbs or outskirts of big city",..: NA 4 3 3 3 4 3 3 3 2 ...
## ..- attr(*, "label")= chr "Domicile, respondent's description"
## $ region : Factor w/ 2046 levels "Not available",..: 1820 1820 1894 1983 1998 1894 1876 1967 1998 1941 ...
## ..- attr(*, "label")= chr "Region"
## $ health_disability: Factor w/ 2 levels "No","Yes": 1 2 1 1 1 1 1 1 1 2 ...
## $ left_right : Factor w/ 3 levels "Left (0-3)","Moderate (4-6)",..: 3 1 2 1 1 NA 2 2 2 3 ...
## $ polintr_binary : Factor w/ 2 levels "Yes","No": 1 1 1 1 2 2 2 1 1 2 ...
## $ life_sat : Factor w/ 3 levels "High","Moderate",..: 1 1 1 1 2 1 1 1 1 1 ...
## $ econ_sat : Factor w/ 3 levels "High","Moderate",..: 1 2 2 3 3 1 2 1 1 2 ...
## $ immig_support : Factor w/ 3 levels "High","Moderate",..: 3 NA 2 3 2 1 2 2 1 2 ...
## $ daily_netuse : Factor w/ 2 levels "No","Yes": 2 2 2 1 2 1 2 2 2 1 ...
## $ country_attach : Factor w/ 3 levels "High","Moderate",..: 1 1 1 1 3 1 1 3 3 1 ...
## $ post_polonline : Factor w/ 2 levels "No","Yes": 1 1 2 2 2 1 1 1 1 1 ...
## $ target_diff : num [1:6996] -3 -2 0 0 4 NA -1 -2 0 -6 ...
## $ trust_europ2 : num [1:6996] 5 0.1 5 0.1 6 NA 2 3 7 2 ...
## $ trust_parliament2: num [1:6996] 8 2 5 0.1 2 9 3 5 7 8 ...
## $ target_ratio : num [1:6996] 0.625 0.05 1 1 3 ...
## $ target_ratiolog : num [1:6996] -0.47 -3 0 0 1.1 ...Now we impute the remaining values with mice:
# Excluding target variable and other useless variables
imputation_data <- gb_clean_regress |>
select(-any_of(c("cntry", "anweight",
"trust_europ", "trust_parliament",
"target_diff", "trust_europ2", "trust_parliament2", "target_ratio", "target_ratiolog")))
# Running it with the default methods
# By default: numerical variables -> pmm, binary factors -> logreg, > 2 levels factors -> polyreg
# WARNING - computationally expensive. This is run with m=1 to print the html output for display. But should be run with m=5 or m-10. With these figures, it can take 30+ minutes to run
imputed_data <- mice(imputation_data, m = 1, seed=1234)##
## iter imp variable
## 1 1 age_rec educ_level income_group income_stress area_type health_disability left_right polintr_binary life_sat econ_sat immig_support daily_netuse country_attach post_polonline
## 2 1 age_rec educ_level income_group income_stress area_type health_disability left_right polintr_binary life_sat econ_sat immig_support daily_netuse country_attach post_polonline
## 3 1 age_rec educ_level income_group income_stress area_type health_disability left_right polintr_binary life_sat econ_sat immig_support daily_netuse country_attach post_polonline
## 4 1 age_rec educ_level income_group income_stress area_type health_disability left_right polintr_binary life_sat econ_sat immig_support daily_netuse country_attach post_polonline
## 5 1 age_rec educ_level income_group income_stress area_type health_disability left_right polintr_binary life_sat econ_sat immig_support daily_netuse country_attach post_polonline## Warning: Number of logged events: 70## Class: mids
## Number of multiple imputations: 1
## Imputation methods:
## essround gender age_rec educ_level
## "" "" "polyreg" "polyreg"
## income_group income_stress area_type region
## "polyreg" "polyreg" "polyreg" ""
## health_disability left_right polintr_binary life_sat
## "logreg" "polyreg" "logreg" "polyreg"
## econ_sat immig_support daily_netuse country_attach
## "polyreg" "polyreg" "logreg" "polyreg"
## post_polonline
## "logreg"
## PredictorMatrix:
## essround gender age_rec educ_level income_group income_stress
## essround 0 1 1 1 1 1
## gender 1 0 1 1 1 1
## age_rec 1 1 0 1 1 1
## educ_level 1 1 1 0 1 1
## income_group 1 1 1 1 0 1
## income_stress 1 1 1 1 1 0
## area_type region health_disability left_right polintr_binary
## essround 1 1 1 1 1
## gender 1 1 1 1 1
## age_rec 1 1 1 1 1
## educ_level 1 1 1 1 1
## income_group 1 1 1 1 1
## income_stress 1 1 1 1 1
## life_sat econ_sat immig_support daily_netuse country_attach
## essround 1 1 1 1 1
## gender 1 1 1 1 1
## age_rec 1 1 1 1 1
## educ_level 1 1 1 1 1
## income_group 1 1 1 1 1
## income_stress 1 1 1 1 1
## post_polonline
## essround 1
## gender 1
## age_rec 1
## educ_level 1
## income_group 1
## income_stress 1
## Number of logged events: 70
## it im dep meth
## 1 1 1 age_rec polyreg
## 2 1 1 educ_level polyreg
## 3 1 1 income_group polyreg
## 4 1 1 income_stress polyreg
## 5 1 1 area_type polyreg
## 6 1 1 health_disability logreg
## out
## 1 genderNo answer, income_stressRefusal, income_stressDon't know, income_stressNo answer, area_typeRefusal, area_typeDon't know, area_typeNo answer, regionAlbania, regionShqipëria, regionVeri, regionDibër, regionDurrës, regionKukës, regionLezhë, regionShkodër, regionQender, regionElbasan, regionTiranë, regionJug, regionBerat, regionFier, regionGjirokastër, regionKorcë, regionVlorë, regionExtra-Regio NUTS 1, regionExtra-Regio NUTS 2, regionExtra-Regio NUTS 3, regionAustria, regionOstösterreich, regionBurgenland, regionMittelburgenland, regionNordburgenland, regionSüdburgenland, regionNiederösterreich, regionMostviertel-Eisenwurzen, regionNiederösterreich-Süd, regionSankt Pölten, regionWaldviertel, regionWeinviertel, regionWiener Umland/Nordteil, regionWiener Umland/Südteil, regionWien, regionSüdösterreich, regionKärnten, regionKlagenfurt-Villach, regionOberkärnten, regionUnterkärnten, regionSteiermark, regionGraz, regionLiezen, regionÖstliche Obersteiermark, regionOststeiermark, regionWest- und Südsteiermark, regionWestliche Obersteiermark, regionWestösterreich, regionOberösterreich, regionInnviertel, regionLinz-Wels, regionMühlviertel, regionSteyr-Kirchdorf, regionTraunviertel, regionSalzburg, regionLungau, regionPinzgau-Pongau, regionSalzburg und Umgebung, regionTirol, regionAußerfern, regionInnsbruck, regionOsttirol, regionTiroler Oberland, regionTiroler Unterland, regionVorarlberg, regionBludenz-Bregenzer Wald, regionRheintal-Bodenseegebiet, regionBelgium, regionRégion de Bruxelles-Capitale/Brussels Hoofdstedelijk Gewest, regionRégion de Bruxelles-Capitale/ Brussels Hoofdstedelijk Gewest, regionArr. de Bruxelles-Capitale/Arr. Brussel-Hoofdstad, regionVlaams Gewest, regionProv. Antwerpen, regionArr. Antwerpen, regionArr. Mechelen, regionArr. Turnhout, regionProv. Limburg (BE), regionArr. Tongeren, regionArr. Hasselt, regionArr. Maaseik, regionProv. Oost-Vlaanderen, regionArr. Aalst, regionArr. Dendermonde, regionArr. Eeklo, regionArr. Gent, regionArr. Oudenaarde, regionArr. Sint-Niklaas, regionProv. Vlaams-Brabant, regionArr. Halle-Vilvoorde, regionArr. Leuven, regionProv. West-Vlaanderen, regionArr. Brugge, regionArr. Diksmuide, regionArr. Ieper, regionArr. Kortrijk, regionArr. Oostende, regionArr. Roeselare, regionArr. Tielt, regionArr. Veurne, regionRégion wallonne, regionProv. Brabant Wallon, regionArr. Nivelles, regionProv. Hainaut, regionArr. Mons, regionArr. Tournai-Mouscron, regionArr. La Louvière, regionArr. Ath, regionArr. Charleroi, regionArr. Soignies, regionArr. Thuin, regionProv. Liège, regionArr. Huy, regionArr. Liège, regionArr. Waremme, regionArr. Verviers — communes francophones, regionBezirk Verviers — Deutschsprachige Gemeinschaft, regionProv. Luxembourg (BE), regionArr. Arlon, regionArr. Bastogne, regionArr. Marche-en-Famenne, regionArr. Neufchâteau, regionArr. Virton, regionProv. Namur, regionArr. Dinant, regionArr. Namur, regionArr. 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## 2 genderNo answer, income_stressRefusal, income_stressDon't know, income_stressNo answer, area_typeRefusal, area_typeDon't know, area_typeNo answer, regionAlbania, regionShqipëria, regionVeri, regionDibër, regionDurrës, regionKukës, regionLezhë, regionShkodër, regionQender, regionElbasan, regionTiranë, regionJug, regionBerat, regionFier, regionGjirokastër, regionKorcë, regionVlorë, regionExtra-Regio NUTS 1, regionExtra-Regio NUTS 2, regionExtra-Regio NUTS 3, regionAustria, regionOstösterreich, regionBurgenland, regionMittelburgenland, regionNordburgenland, regionSüdburgenland, regionNiederösterreich, regionMostviertel-Eisenwurzen, regionNiederösterreich-Süd, regionSankt Pölten, regionWaldviertel, regionWeinviertel, regionWiener Umland/Nordteil, regionWiener Umland/Südteil, regionWien, regionSüdösterreich, regionKärnten, regionKlagenfurt-Villach, regionOberkärnten, regionUnterkärnten, regionSteiermark, regionGraz, regionLiezen, regionÖstliche Obersteiermark, regionOststeiermark, regionWest- und Südsteiermark, regionWestliche Obersteiermark, regionWestösterreich, regionOberösterreich, regionInnviertel, regionLinz-Wels, regionMühlviertel, regionSteyr-Kirchdorf, regionTraunviertel, regionSalzburg, regionLungau, regionPinzgau-Pongau, regionSalzburg und Umgebung, regionTirol, regionAußerfern, regionInnsbruck, regionOsttirol, regionTiroler Oberland, regionTiroler Unterland, regionVorarlberg, regionBludenz-Bregenzer Wald, regionRheintal-Bodenseegebiet, regionBelgium, regionRégion de Bruxelles-Capitale/Brussels Hoofdstedelijk Gewest, regionRégion de Bruxelles-Capitale/ Brussels Hoofdstedelijk Gewest, regionArr. de Bruxelles-Capitale/Arr. 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Verviers — communes francophones, regionBezirk Verviers — Deutschsprachige Gemeinschaft, regionProv. Luxembourg (BE), regionArr. Arlon, regionArr. Bastogne, regionArr. Marche-en-Famenne, regionArr. Neufchâteau, regionArr. Virton, regionProv. Namur, regionArr. Dinant, regionArr. Namur, regionArr. Philippeville, regionBulgaria, regionСеверна и Югоизточна България, regionСеверозападен, regionВидин, regionМонтана, regionВраца, regionПлевен, regionЛовеч, regionСеверен централен, regionВелико Търново, regionГаброво, regionРусе, regionРазград, regionСилистра, regionСевероизточен, regionВарна, regionДобрич, regionШумен, regionТърговище, regionЮгоизточен, regionБургас, regionСливен, regionЯмбол, regionСтара Загора, regionЮгозападна и Южна централна България, regionЮгозападен, regionСофия (столица), regionСофия, regionБлагоевград, regionПерник, regionКюстендил, regionЮжен централен, regionПловдив, regionХасково, regionПазарджик, regionСмолян, regionКърджали, regionSwitzerland, regionSchweiz/Suisse/Svizzera, regionRégion lémanique, regionVaud, regionValais / Wallis, regionGenève, regionEspace Mittelland, regionBern / Berne, regionFribourg / Freiburg, regionSolothurn, regionNeuchâtel, regionJura, regionNordwestschweiz, regionBasel-Stadt, regionBasel-Landschaft, regionAargau, regionZürich, regionOstschweiz, regionGlarus, regionSchaffhausen, regionAppenzell Ausserrhoden, regionAppenzell Innerrhoden, regionSt. Gallen, regionGraubünden / Grigioni / Grischun, regionThurgau, regionZentralschweiz, regionLuzern, regionUri, regionSchwyz, regionObwalden, regionNidwalden, regionZug, regionTicino, regionCyprus, regionΚύπρος, regionCzechia, regionČesko, regionPraha, regionHlavní město Praha, regionStřední Čechy, regionStředočeský kraj, regionJihozápad, regionJihočeský kraj, regionPlzeňský kraj, regionSeverozápad, regionKarlovarský kraj, regionÚstecký kraj, regionSeverovýchod, regionLiberecký kraj, regionKrálovéhradecký kraj, regionPardubický kraj, regionJihovýchod, regionKraj Vysočina, regionJihomoravský kraj, regionStřední Morava, regionOlomoucký kraj, regionZlínský kraj, regionMoravskoslezsko, regionMoravskoslezský kraj, regionGermany, regionBaden-Württemberg, regionStuttgart, regionStuttgart, Stadtkreis, regionBöblingen, regionEsslingen, regionGöppingen, regionLudwigsburg, regionRems-Murr-Kreis, regionHeilbronn, Stadtkreis, regionHeilbronn, Landkreis, regionHohenlohekreis, regionSchwäbisch Hall, regionMain-Tauber-Kreis, regionHeidenheim, regionOstalbkreis, regionKarlsruhe, regionBaden-Baden, Stadtkreis, regionKarlsruhe, Stadtkreis, regionKarlsruhe, Landkreis, regionRastatt, regionHeidelberg, Stadtkreis, regionMannheim, Stadtkreis, regionNeckar-Odenwald-Kreis, regionRhein-Neckar-Kreis, regionPforzheim, Stadtkreis, regionCalw, regionEnzkreis, regionFreudenstadt, regionFreiburg, regionFreiburg im Breisgau, Stadtkreis, regionBreisgau-Hochschwarzwald, regionEmmendingen, regionOrtenaukreis, regionRottweil, regionSchwarzwald-Baar-Kreis, regionTuttlingen, regionKonstanz, regionLörrach, regionWaldshut, regionTübingen, regionReutlingen, regionTübingen, Landkreis, regionZollernalbkreis, regionUlm, Stadtkreis, regionAlb-Donau-Kreis, regionBiberach, regionBodenseekreis, regionRavensburg, regionSigmaringen, regionBayern, regionOberbayern, regionIngolstadt, Kreisfreie Stadt, regionMünchen, Kreisfreie Stadt, regionRosenheim, Kreisfreie Stadt, regionAltötting, regionBerchtesgadener Land, regionBad Tölz-Wolfratshausen, regionDachau, regionEbersberg, regionEichstätt, regionErding, regionFreising, regionFürstenfeldbruck, regionGarmisch-Partenkirchen, regionLandsberg am Lech, regionMiesbach, regionMühldorf a. 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Waldnaab, regionRegensburg, Landkreis, regionSchwandorf, regionTirschenreuth, regionOberfranken, regionBamberg, Kreisfreie Stadt, regionBayreuth, Kreisfreie Stadt, regionCoburg, Kreisfreie Stadt, regionHof, Kreisfreie Stadt, regionBamberg, Landkreis, regionBayreuth, Landkreis, regionCoburg, Landkreis, regionForchheim, regionHof, Landkreis, regionKronach, regionKulmbach, regionLichtenfels, regionWunsiedel i. Fichtelgebirge, regionMittelfranken, regionAnsbach, Kreisfreie Stadt, regionErlangen, Kreisfreie Stadt, regionFürth, Kreisfreie Stadt, regionNürnberg, Kreisfreie Stadt, regionSchwabach, Kreisfreie Stadt, regionAnsbach, Landkreis, regionErlangen-Höchstadt, regionFürth, Landkreis, regionNürnberger Land, regionNeustadt a. d. Aisch-Bad Windsheim, regionRoth, regionWeißenburg-Gunzenhausen, regionUnterfranken, regionAschaffenburg, Kreisfreie Stadt, regionSchweinfurt, Kreisfreie Stadt, regionWürzburg, Kreisfreie Stadt, regionAschaffenburg, Landkreis, regionBad Kissingen, regionRhön-Grabfeld, regionHaßberge, regionKitzingen, regionMiltenberg, regionMain-Spessart, regionSchweinfurt, Landkreis, regionWürzburg, Landkreis, regionSchwaben, regionAugsburg, Kreisfreie Stadt, regionKaufbeuren, Kreisfreie Stadt, regionKempten (Allgäu), Kreisfreie Stadt, regionMemmingen, Kreisfreie Stadt, regionAichach-Friedberg, regionAugsburg, Landkreis, regionDillingen a.d. 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regionHannover, regionDiepholz, regionHameln-Pyrmont, regionHildesheim, regionHolzminden, regionNienburg (Weser), regionSchaumburg, regionRegion Hannover, regionLüneburg, regionCelle, regionCuxhaven, regionHarburg, regionLüchow-Dannenberg, regionLüneburg, Landkreis, regionOsterholz, regionRotenburg (Wümme), regionHeidekreis, regionStade, regionUelzen, regionVerden, regionWeser-Ems, regionDelmenhorst, Kreisfreie Stadt, regionEmden, Kreisfreie Stadt, regionOldenburg (Oldenburg), Kreisfreie Stadt, regionOsnabrück, Kreisfreie Stadt, regionWilhelmshaven, Kreisfreie Stadt, regionAmmerland, regionAurich, regionCloppenburg, regionEmsland, regionFriesland (DE), regionGrafschaft Bentheim, regionLeer, regionOldenburg, Landkreis, regionOsnabrück, Landkreis, regionVechta, regionWesermarsch, regionWittmund, regionNordrhein-Westfalen, regionDüsseldorf, regionDüsseldorf, Kreisfreie Stadt, regionDuisburg, Kreisfreie Stadt, regionEssen, Kreisfreie Stadt, regionKrefeld, Kreisfreie Stadt, regionMönchengladbach, Kreisfreie Stadt, regionMülheim an der Ruhr, Kreisfreie Stadt, regionOberhausen, Kreisfreie Stadt, regionRemscheid, Kreisfreie Stadt, regionSolingen, Kreisfreie Stadt, regionWuppertal, Kreisfreie Stadt, regionKleve, regionMettmann, regionRhein-Kreis Neuss, regionViersen, regionWesel, regionKöln, regionBonn, Kreisfreie Stadt, regionKöln, Kreisfreie Stadt, regionLeverkusen, Kreisfreie Stadt, regionDüren, regionRhein-Erft-Kreis, regionEuskirchen, regionHeinsberg, regionOberbergischer Kreis, regionRheinisch-Bergischer Kreis, regionRhein-Sieg-Kreis, regionStädteregion Aachen, regionMünster, regionBottrop, Kreisfreie Stadt, regionGelsenkirchen, Kreisfreie Stadt, regionMünster, Kreisfreie Stadt, regionBorken, regionCoesfeld, regionRecklinghausen, regionSteinfurt, regionWarendorf, regionDetmold, regionBielefeld, Kreisfreie Stadt, regionGütersloh, regionHerford, regionHöxter, regionLippe, regionMinden-Lübbecke, regionPaderborn, regionArnsberg, regionBochum, Kreisfreie Stadt, regionDortmund, Kreisfreie Stadt, regionHagen, Kreisfreie Stadt, regionHamm, Kreisfreie Stadt, regionHerne, Kreisfreie Stadt, regionEnnepe-Ruhr-Kreis, regionHochsauerlandkreis, regionMärkischer Kreis, regionOlpe, regionSiegen-Wittgenstein, regionSoest, regionUnna, regionRheinland-Pfalz, regionKoblenz, regionKoblenz, Kreisfreie Stadt, regionAhrweiler, regionAltenkirchen (Westerwald), regionBad Kreuznach, regionBirkenfeld, regionMayen-Koblenz, regionNeuwied, regionRhein-Lahn-Kreis, regionWesterwaldkreis, regionCochem-Zell, regionRhein-Hunsrück-Kreis, regionTrier, regionTrier, Kreisfreie Stadt, regionBernkastel-Wittlich, regionEifelkreis Bitburg-Prüm, regionVulkaneifel, regionTrier-Saarburg, regionRheinhessen-Pfalz, regionFrankenthal (Pfalz), Kreisfreie Stadt, regionKaiserslautern, Kreisfreie Stadt, regionLandau in der Pfalz, Kreisfreie Stadt, regionLudwigshafen am Rhein, Kreisfreie Stadt, regionMainz, Kreisfreie Stadt, regionNeustadt an der Weinstraße, Kreisfreie Stadt, regionPirmasens, Kreisfreie Stadt, regionSpeyer, Kreisfreie Stadt, regionWorms, Kreisfreie Stadt, regionZweibrücken, Kreisfreie Stadt, regionAlzey-Worms, regionBad Dürkheim, regionDonnersbergkreis, regionGermersheim, regionKaiserslautern, Landkreis, regionKusel, regionSüdliche Weinstraße, regionRhein-Pfalz-Kreis, regionMainz-Bingen, regionSüdwestpfalz, regionSaarland, regionRegionalverband Saarbrücken, regionMerzig-Wadern, regionNeunkirchen, regionSaarlouis, regionSaarpfalz-Kreis, regionSt. Wendel, regionSachsen, regionDresden, regionDresden, Kreisfreie Stadt, regionBautzen, regionGörlitz, regionMeißen, regionSächsische Schweiz-Osterzgebirge, regionChemnitz, regionChemnitz, Kreisfreie Stadt, regionErzgebirgskreis, regionMittelsachsen, regionVogtlandkreis, regionZwickau, regionLeipzig, regionLeipzig, Kreisfreie Stadt, regionNordsachsen, regionSachsen-Anhalt, regionDessau-Roßlau, Kreisfreie Stadt, regionHalle (Saale), Kreisfreie Stadt, regionMagdeburg, Kreisfreie Stadt, regionAltmarkkreis Salzwedel, regionAnhalt-Bitterfeld, regionJerichower Land, regionBörde, regionBurgenlandkreis, regionHarz, regionMansfeld-Südharz, regionSaalekreis, regionSalzlandkreis, regionStendal, regionWittenberg, regionSchleswig-Holstein, regionFlensburg, Kreisfreie Stadt, regionKiel, Kreisfreie Stadt, regionLübeck, Kreisfreie Stadt, regionNeumünster, Kreisfreie Stadt, regionDithmarschen, regionHerzogtum Lauenburg, regionNordfriesland, regionOstholstein, regionPinneberg, regionPlön, regionRendsburg-Eckernförde, regionSchleswig-Flensburg, regionSegeberg, regionSteinburg, regionStormarn, regionThüringen, regionErfurt, Kreisfreie Stadt, regionGera, Kreisfreie Stadt, regionJena, Kreisfreie Stadt, regionSuhl, Kreisfreie Stadt, regionWeimar, Kreisfreie Stadt, regionEichsfeld, regionNordhausen, regionUnstrut-Hainich-Kreis, regionKyffhäuserkreis, regionSchmalkalden-Meiningen, regionGotha, regionSömmerda, regionHildburghausen, regionIlm-Kreis, regionWeimarer Land, regionSonneberg, regionSaalfeld-Rudolstadt, regionSaale-Holzland-Kreis, regionSaale-Orla-Kreis, regionGreiz, regionAltenburger Land, regionEisenach, Kreisfreie Stadt, regionWartburgkreis, regiondeprecated codes, regionDenmark, regionDanmark, regionHovedstaden, regionByen København, regionKøbenhavns omegn, regionNordsjælland, regionBornholm, regionSjælland, regionØstsjælland, regionVest- og Sydsjælland, regionSyddanmark, regionFyn, regionSydjylland, regionMidtjylland, regionVestjylland, regionØstjylland, regionNordjylland, regionEstonia, regionEesti, regionPõhja-Eesti, regionLääne-Eesti, regionKesk-Eesti, regionKirde-Eesti, regionLõuna-Eesti, regionΑττική, regionAττική, regionΒόρειος Τομέας Αθηνών, regionΔυτικός Τομέας Αθηνών, regionΚεντρικός Τομέας Αθηνών, regionΝότιος Τομέας Αθηνών, regionΑνατολική Αττική, regionΔυτική Αττική, regionΠειραιάς, Νήσοι, regionΝησιά Αιγαίου, Κρήτη, regionΒόρειο Αιγαίο, regionΛέσβος, Λήμνος, regionΙκαρία, Σάμος, regionΧίος, regionΝότιο Αιγαίο, regionΚάλυμνος, Κάρπαθος – Ηρωική Νήσος Κάσος, Κως, Ρόδος, regionΆνδρος, Θήρα, Κέα, Μήλος, Μύκονος, Νάξος, Πάρος, Σύρος, Τήνος, regionΚρήτη, regionΗράκλειο, regionΛασίθι, regionΡέθυμνο, regionΧανιά, regionΒόρεια Ελλάδα, regionAνατολική Μακεδονία, Θράκη, regionΈβρος, regionΞάνθη, regionΡοδόπη, regionΔράμα, regionΘάσος, Καβάλα, regionΚεντρική Μακεδονία, regionΗμαθία, regionΘεσσαλονίκη, regionΚιλκίς, regionΠέλλα, regionΠιερία, regionΣέρρες, regionΧαλκιδική, regionΔυτική Μακεδονία, regionΓρεβενά, Κοζάνη, regionΚαστοριά, regionΦλώρινα, regionΉπειρος, regionΆρτα, Πρέβεζα, regionΘεσπρωτία, regionΙωάννινα, regionΚεντρική Ελλάδα, regionΘεσσαλία, regionΚαρδίτσα, Τρίκαλα, regionΛάρισα, regionΜαγνησία, Σποράδες, regionΙόνια Νησιά, regionΖάκυνθος, regionΚέρκυρα, regionΙθάκη, Κεφαλληνία, regionΛευκάδα, regionΔυτική Ελλάδα, regionΑιτωλοακαρνανία, regionΑχαΐα, regionΗλεία, regionΣτερεά Ελλάδα, regionΒοιωτία, regionΕύβοια, regionΕυρυτανία, regionΦθιώτιδα, regionΦωκίδα, regionΠελοπόννησος, regionΑργολίδα, Αρκαδία, regionΚορινθία, 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## 3 genderNo answer, income_stressRefusal, income_stressDon't know, income_stressNo answer, area_typeRefusal, area_typeDon't know, area_typeNo answer, regionAlbania, regionShqipëria, regionVeri, regionDibër, regionDurrës, regionKukës, regionLezhë, regionShkodër, regionQender, regionElbasan, regionTiranë, regionJug, regionBerat, regionFier, regionGjirokastër, regionKorcë, regionVlorë, regionExtra-Regio NUTS 1, regionExtra-Regio NUTS 2, regionExtra-Regio NUTS 3, regionAustria, regionOstösterreich, regionBurgenland, regionMittelburgenland, regionNordburgenland, regionSüdburgenland, regionNiederösterreich, regionMostviertel-Eisenwurzen, regionNiederösterreich-Süd, regionSankt Pölten, regionWaldviertel, regionWeinviertel, regionWiener Umland/Nordteil, regionWiener Umland/Südteil, regionWien, regionSüdösterreich, regionKärnten, regionKlagenfurt-Villach, regionOberkärnten, regionUnterkärnten, regionSteiermark, regionGraz, regionLiezen, regionÖstliche Obersteiermark, regionOststeiermark, regionWest- und Südsteiermark, regionWestliche Obersteiermark, regionWestösterreich, regionOberösterreich, regionInnviertel, regionLinz-Wels, regionMühlviertel, regionSteyr-Kirchdorf, regionTraunviertel, regionSalzburg, regionLungau, regionPinzgau-Pongau, regionSalzburg und Umgebung, regionTirol, regionAußerfern, regionInnsbruck, regionOsttirol, regionTiroler Oberland, regionTiroler Unterland, regionVorarlberg, regionBludenz-Bregenzer Wald, regionRheintal-Bodenseegebiet, regionBelgium, regionRégion de Bruxelles-Capitale/Brussels Hoofdstedelijk Gewest, regionRégion de Bruxelles-Capitale/ Brussels Hoofdstedelijk Gewest, regionArr. de Bruxelles-Capitale/Arr. 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Donau, regionGünzburg, regionNeu-Ulm, regionLindau (Bodensee), regionOstallgäu, regionUnterallgäu, regionDonau-Ries, regionOberallgäu, regionBerlin, regionBrandenburg, regionBrandenburg an der Havel, Kreisfreie Stadt, regionCottbus, Kreisfreie Stadt, regionFrankfurt (Oder), Kreisfreie Stadt, regionPotsdam, Kreisfreie Stadt, regionBarnim, regionDahme-Spreewald, regionElbe-Elster, regionHavelland, regionMärkisch-Oderland, regionOberhavel, regionOberspreewald-Lausitz, regionOder-Spree, regionOstprignitz-Ruppin, regionPotsdam-Mittelmark, regionPrignitz, regionSpree-Neiße, regionTeltow-Fläming, regionUckermark, regionBremen, regionBremen, Kreisfreie Stadt, regionBremerhaven, Kreisfreie Stadt, regionHamburg, regionHessen, regionDarmstadt, regionDarmstadt, Kreisfreie Stadt, regionFrankfurt am Main, Kreisfreie Stadt, regionOffenbach am Main, Kreisfreie Stadt, regionWiesbaden, Kreisfreie Stadt, regionBergstraße, regionDarmstadt-Dieburg, regionGroß-Gerau, regionHochtaunuskreis, 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regionHannover, regionDiepholz, regionHameln-Pyrmont, regionHildesheim, regionHolzminden, regionNienburg (Weser), regionSchaumburg, regionRegion Hannover, regionLüneburg, regionCelle, regionCuxhaven, regionHarburg, regionLüchow-Dannenberg, regionLüneburg, Landkreis, regionOsterholz, regionRotenburg (Wümme), regionHeidekreis, regionStade, regionUelzen, regionVerden, regionWeser-Ems, regionDelmenhorst, Kreisfreie Stadt, regionEmden, Kreisfreie Stadt, regionOldenburg (Oldenburg), Kreisfreie Stadt, regionOsnabrück, Kreisfreie Stadt, regionWilhelmshaven, Kreisfreie Stadt, regionAmmerland, regionAurich, regionCloppenburg, regionEmsland, regionFriesland (DE), regionGrafschaft Bentheim, regionLeer, regionOldenburg, Landkreis, regionOsnabrück, Landkreis, regionVechta, regionWesermarsch, regionWittmund, regionNordrhein-Westfalen, regionDüsseldorf, regionDüsseldorf, Kreisfreie Stadt, regionDuisburg, Kreisfreie Stadt, regionEssen, Kreisfreie Stadt, regionKrefeld, Kreisfreie Stadt, regionMönchengladbach, Kreisfreie Stadt, regionMülheim an der Ruhr, Kreisfreie Stadt, regionOberhausen, Kreisfreie Stadt, regionRemscheid, Kreisfreie Stadt, regionSolingen, Kreisfreie Stadt, regionWuppertal, Kreisfreie Stadt, regionKleve, regionMettmann, regionRhein-Kreis Neuss, regionViersen, regionWesel, regionKöln, regionBonn, Kreisfreie Stadt, regionKöln, Kreisfreie Stadt, regionLeverkusen, Kreisfreie Stadt, regionDüren, regionRhein-Erft-Kreis, regionEuskirchen, regionHeinsberg, regionOberbergischer Kreis, regionRheinisch-Bergischer Kreis, regionRhein-Sieg-Kreis, regionStädteregion Aachen, regionMünster, regionBottrop, Kreisfreie Stadt, regionGelsenkirchen, Kreisfreie Stadt, regionMünster, Kreisfreie Stadt, regionBorken, regionCoesfeld, regionRecklinghausen, regionSteinfurt, regionWarendorf, regionDetmold, regionBielefeld, Kreisfreie Stadt, regionGütersloh, regionHerford, regionHöxter, regionLippe, regionMinden-Lübbecke, regionPaderborn, regionArnsberg, regionBochum, Kreisfreie Stadt, regionDortmund, Kreisfreie Stadt, regionHagen, Kreisfreie Stadt, regionHamm, Kreisfreie Stadt, regionHerne, Kreisfreie Stadt, regionEnnepe-Ruhr-Kreis, regionHochsauerlandkreis, regionMärkischer Kreis, regionOlpe, regionSiegen-Wittgenstein, regionSoest, regionUnna, regionRheinland-Pfalz, regionKoblenz, regionKoblenz, Kreisfreie Stadt, regionAhrweiler, regionAltenkirchen (Westerwald), regionBad Kreuznach, regionBirkenfeld, regionMayen-Koblenz, regionNeuwied, regionRhein-Lahn-Kreis, regionWesterwaldkreis, regionCochem-Zell, regionRhein-Hunsrück-Kreis, regionTrier, regionTrier, Kreisfreie Stadt, regionBernkastel-Wittlich, regionEifelkreis Bitburg-Prüm, regionVulkaneifel, regionTrier-Saarburg, regionRheinhessen-Pfalz, regionFrankenthal (Pfalz), Kreisfreie Stadt, regionKaiserslautern, Kreisfreie Stadt, regionLandau in der Pfalz, Kreisfreie Stadt, regionLudwigshafen am Rhein, Kreisfreie Stadt, regionMainz, Kreisfreie Stadt, regionNeustadt an der Weinstraße, Kreisfreie Stadt, 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regionEge, regionİzmir, regionAydın, Denizli, Muğla, regionAydın, regionDenizli, regionMuğla, regionManisa, Afyonkarahisar, Kütahya, Uşak, regionManisa, regionAfyonkarahisar, regionKütahya, regionUşak, regionDoğu Marmara, regionBursa, Eskişehir, Bilecik, regionBursa, regionEskişehir, regionBilecik, regionKocaeli, Sakarya, Düzce, Bolu, Yalova, regionKocaeli, regionSakarya, regionDüzce, regionBolu, regionYalova, regionBatı Anadolu, regionAnkara, regionKonya, Karaman, regionKonya, regionKaraman, regionAkdeniz, regionAntalya, Isparta, Burdur, regionAntalya, regionIsparta, regionBurdur, regionAdana, Mersin, regionAdana, regionMersin, regionHatay, Kahramanmaraş, Osmaniye, regionHatay, regionKahramanmaraş, regionOsmaniye, regionOrta Anadolu, regionKırıkkale, Aksaray, Niğde, Nevşehir, Kırşehir, regionKırıkkale, regionAksaray, regionNiğde, regionNevşehir, regionKırşehir, regionKayseri, Sivas, Yozgat, regionKayseri, regionSivas, regionYozgat, regionBatı Karadeniz, regionZonguldak, Karabük, Bartın, regionZonguldak, regionKarabük, regionBartın, regionKastamonu, Çankırı, Sinop, regionKastamonu, regionÇankırı, regionSinop, regionSamsun, Tokat, Çorum, Amasya, regionSamsun, regionTokat, regionÇorum, regionAmasya, regionDoğu Karadeniz, regionTrabzon, Ordu, Giresun, Rize, Artvin, Gümüşhane, regionTrabzon, regionOrdu, regionGiresun, regionRize, regionArtvin, regionGümüşhane, regionKuzeydoğu Anadolu, regionErzurum, Erzincan, Bayburt, regionErzurum, regionErzincan, regionBayburt, regionAğrı, Kars, Iğdır, Ardahan, regionAğrı, regionKars, regionIğdır, regionArdahan, regionOrtadoğu Anadolu, regionMalatya, Elazığ, Bingöl, Tunceli, regionMalatya, regionElazığ, regionBingöl, regionTunceli, regionVan, Muş, Bitlis, Hakkari, regionVan, regionMuş, regionBitlis, regionHakkari, regionGüneydoğu Anadolu, regionGaziantep, Adıyaman, Kilis, regionGaziantep, regionAdıyaman, regionKilis, regionŞanlıurfa, Diyarbakır, regionŞanlıurfa, regionDiyarbakır, regionMardin, Batman, Şırnak, Siirt, regionMardin, regionBatman, regionŞırnak, regionSiirt, regionUkraine, regionCrimea, Autonomy Republic, regionVynnytska oblast, regionVolynska oblast, regionDnipropetrovska oblast, regionDonetska oblast, regionZhytomyrska oblast, regionZakarpatska oblast, regionZaporizska oblast, regionIvano-Frankivska oblast, regionKyivska oblast, regionKirovogradska oblast, regionLuganska oblast, regionLvivska oblast, regionMykolaivska oblast, regionOdesska oblast, regionPoltavska oblast, regionRivenska oblast, regionSumska oblast, regionTernopilska oblast, regionKharkivska oblast, regionKhersonska oblast, regionKhmelnitska oblast, regionCherkasska oblast, regionChernovytska oblast, regionChernigivska oblast, regionKyiv city, regionTees Valley and Durham, regionHartlepool and Stockton-on-Tees, regionSouth Teesside, regionDarlington, regionDurham CC, regionNorthumberland and Tyne and Wear, regionNorthumberland, regionTyneside, regionSunderland, regionCumbria, regionWest Cumbria, regionEast Cumbria, regionGreater Manchester, regionManchester, regionGreater Manchester South West, regionGreater Manchester South East, regionGreater Manchester North West, regionGreater Manchester North East, regionLancashire, regionBlackburn with Darwen, regionBlackpool, regionLancaster and Wyre, regionMid Lancashire, regionEast Lancashire, regionChorley and West Lancashire, regionCheshire, regionWarrington, regionCheshire East, regionCheshire West and Chester, regionMerseyside, regionEast Merseyside, regionLiverpool, regionSefton, regionWirral, regionEast Yorkshire and Northern Lincolnshire, regionKingston upon Hull, City of, regionEast Riding of Yorkshire, regionNorth and North East Lincolnshire, regionNorth Yorkshire, regionYork, regionNorth Yorkshire CC, regionSouth Yorkshire, regionBarnsley, Doncaster and Rotherham, regionSheffield, regionWest Yorkshire, regionBradford, regionLeeds, regionCalderdale and Kirklees, regionWakefield, regionDerbyshire and Nottinghamshire, regionDerby, regionEast Derbyshire, 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regionSouthend-on-Sea, regionThurrock, regionEssex Haven Gateway, regionWest Essex, regionHeart of Essex, regionEssex Thames Gateway, regionInner London — West, regionCamden and City of London, regionWestminster, regionKensington & Chelsea and Hammersmith & Fulham, regionWandsworth, regionInner London — East, regionHackney and Newham, regionTower Hamlets, regionHaringey and Islington, regionLewisham and Southwark, regionLambeth, regionOuter London — East and North East, regionBexley and Greenwich, regionBarking & Dagenham and Havering, regionRedbridge and Waltham Forest, regionEnfield, regionOuter London — South, regionBromley, regionCroydon, regionMerton, Kingston upon Thames and Sutton, regionOuter London — West and North West, regionBarnet, regionBrent, regionEaling, regionHarrow and Hillingdon, regionHounslow and Richmond upon Thames, regionSouth East (England), regionBerkshire, Buckinghamshire and Oxfordshire, regionBerkshire, regionMilton Keynes, regionBuckinghamshire CC, regionOxfordshire, regionSurrey, East and West Sussex, regionBrighton and Hove, regionEast Sussex CC, regionWest Surrey, regionEast Surrey, regionWest Sussex (South West), regionWest Sussex (North East), regionHampshire and Isle of Wight, regionPortsmouth, regionSouthampton, regionIsle of Wight, regionSouth Hampshire, regionCentral Hampshire, regionNorth Hampshire, regionKent, regionMedway, regionKent Thames Gateway, regionEast Kent, regionMid Kent, regionWest Kent, regionGloucestershire, Wiltshire and Bristol/Bath area, regionBristol, City of, regionBath and North East Somerset, North Somerset and South Gloucestershire, regionGloucestershire, regionSwindon, regionWiltshire CC, regionDorset and Somerset, regionSomerset, regionBournemouth, Christchurch and Poole, regionDorset, regionCornwall and Isles of Scilly, regionDevon, regionPlymouth, regionTorbay, regionDevon CC, regionWest Wales and The Valleys, regionIsle of Anglesey, regionGwynedd, regionConwy and Denbighshire, regionSouth West Wales, regionCentral Valleys, regionGwent Valleys, regionBridgend and Neath Port Talbot, regionSwansea, regionEast Wales, regionMonmouthshire and Newport, regionCardiff and Vale of Glamorgan, regionFlintshire and Wrexham, regionPowys, regionNorth Eastern Scotland, regionAberdeen City and Aberdeenshire, regionHighlands and Islands, regionCaithness & Sutherland and Ross & Cromarty, regionInverness & Nairn and Moray, Badenoch & Strathspey, regionLochaber, Skye & Lochalsh, Arran & Cumbrae and Argyll & Bute, regionNa h-Eileanan Siar (Western Isles), regionOrkney Islands, regionShetland Islands, regionEastern Scotland, regionAngus and Dundee City, regionClackmannanshire and Fife, regionEast Lothian and Midlothian, regionEdinburgh, City of, regionFalkirk, regionPerth & Kinross and Stirling, regionWest Lothian, regionWest Central Scotland, regionEast Dunbartonshire, West Dunbartonshire and Helensburgh & Lomond, regionGlasgow City, regionInverclyde, East Renfrewshire and Renfrewshire, regionNorth Lanarkshire, regionSouthern Scotland, regionScottish Borders, regionDumfries & Galloway, regionEast Ayrshire and North Ayrshire mainland, regionSouth Ayrshire, regionSouth Lanarkshire, regionBelfast, regionArmagh City, Banbridge and Craigavon, regionNewry, Mourne and Down, regionArds and North Down, regionDerry City and Strabane, regionMid Ulster, regionCauseway Coast and Glens, regionAntrim and Newtownabbey, regionLisburn and Castlereagh, regionMid and East Antrim, regionFermanagh and Omagh, regionKosovo, regionPrishtinë, regionMitrovicë, regionPejë, regionPrizren, regionFerizaj, regionGjilan, regionGjakovë
## 4 genderNo answer, area_typeRefusal, area_typeDon't know, area_typeNo answer, regionAlbania, regionShqipëria, regionVeri, regionDibër, regionDurrës, regionKukës, regionLezhë, regionShkodër, regionQender, regionElbasan, regionTiranë, regionJug, regionBerat, regionFier, regionGjirokastër, regionKorcë, regionVlorë, regionExtra-Regio NUTS 1, regionExtra-Regio NUTS 2, regionExtra-Regio NUTS 3, regionAustria, regionOstösterreich, regionBurgenland, regionMittelburgenland, regionNordburgenland, regionSüdburgenland, regionNiederösterreich, regionMostviertel-Eisenwurzen, regionNiederösterreich-Süd, regionSankt Pölten, regionWaldviertel, regionWeinviertel, regionWiener Umland/Nordteil, regionWiener Umland/Südteil, regionWien, regionSüdösterreich, regionKärnten, regionKlagenfurt-Villach, regionOberkärnten, regionUnterkärnten, regionSteiermark, regionGraz, regionLiezen, regionÖstliche Obersteiermark, regionOststeiermark, regionWest- und Südsteiermark, regionWestliche Obersteiermark, regionWestösterreich, regionOberösterreich, regionInnviertel, regionLinz-Wels, regionMühlviertel, regionSteyr-Kirchdorf, regionTraunviertel, regionSalzburg, regionLungau, regionPinzgau-Pongau, regionSalzburg und Umgebung, regionTirol, regionAußerfern, regionInnsbruck, regionOsttirol, regionTiroler Oberland, regionTiroler Unterland, regionVorarlberg, regionBludenz-Bregenzer Wald, regionRheintal-Bodenseegebiet, regionBelgium, regionRégion de Bruxelles-Capitale/Brussels Hoofdstedelijk Gewest, regionRégion de Bruxelles-Capitale/ Brussels Hoofdstedelijk Gewest, regionArr. de Bruxelles-Capitale/Arr. Brussel-Hoofdstad, regionVlaams Gewest, regionProv. Antwerpen, regionArr. Antwerpen, regionArr. Mechelen, regionArr. Turnhout, regionProv. Limburg (BE), regionArr. Tongeren, regionArr. Hasselt, regionArr. Maaseik, regionProv. Oost-Vlaanderen, regionArr. Aalst, regionArr. Dendermonde, regionArr. Eeklo, regionArr. Gent, regionArr. Oudenaarde, regionArr. Sint-Niklaas, regionProv. Vlaams-Brabant, regionArr. Halle-Vilvoorde, regionArr. Leuven, regionProv. West-Vlaanderen, regionArr. Brugge, regionArr. Diksmuide, regionArr. Ieper, regionArr. Kortrijk, regionArr. Oostende, regionArr. Roeselare, regionArr. Tielt, regionArr. Veurne, regionRégion wallonne, regionProv. Brabant Wallon, regionArr. Nivelles, regionProv. Hainaut, regionArr. Mons, regionArr. Tournai-Mouscron, regionArr. La Louvière, regionArr. Ath, regionArr. Charleroi, regionArr. Soignies, regionArr. Thuin, regionProv. Liège, regionArr. Huy, regionArr. Liège, regionArr. Waremme, regionArr. Verviers — communes francophones, regionBezirk Verviers — Deutschsprachige Gemeinschaft, regionProv. Luxembourg (BE), regionArr. Arlon, regionArr. Bastogne, regionArr. Marche-en-Famenne, regionArr. Neufchâteau, regionArr. Virton, regionProv. Namur, regionArr. Dinant, regionArr. Namur, regionArr. Philippeville, regionBulgaria, regionСеверна и Югоизточна България, regionСеверозападен, regionВидин, regionМонтана, regionВраца, regionПлевен, regionЛовеч, regionСеверен централен, regionВелико Търново, regionГаброво, regionРусе, regionРазград, regionСилистра, regionСевероизточен, regionВарна, regionДобрич, regionШумен, regionТърговище, regionЮгоизточен, regionБургас, regionСливен, regionЯмбол, regionСтара Загора, regionЮгозападна и Южна централна България, regionЮгозападен, regionСофия (столица), regionСофия, regionБлагоевград, regionПерник, regionКюстендил, regionЮжен централен, regionПловдив, regionХасково, regionПазарджик, regionСмолян, regionКърджали, regionSwitzerland, regionSchweiz/Suisse/Svizzera, regionRégion lémanique, regionVaud, regionValais / Wallis, regionGenève, regionEspace Mittelland, regionBern / Berne, regionFribourg / Freiburg, regionSolothurn, regionNeuchâtel, regionJura, regionNordwestschweiz, regionBasel-Stadt, regionBasel-Landschaft, regionAargau, regionZürich, regionOstschweiz, regionGlarus, regionSchaffhausen, regionAppenzell Ausserrhoden, regionAppenzell Innerrhoden, regionSt. Gallen, regionGraubünden / Grigioni / Grischun, regionThurgau, regionZentralschweiz, regionLuzern, regionUri, regionSchwyz, regionObwalden, regionNidwalden, regionZug, regionTicino, regionCyprus, regionΚύπρος, regionCzechia, regionČesko, regionPraha, regionHlavní město Praha, regionStřední Čechy, regionStředočeský kraj, regionJihozápad, regionJihočeský kraj, regionPlzeňský kraj, regionSeverozápad, regionKarlovarský kraj, regionÚstecký kraj, regionSeverovýchod, regionLiberecký kraj, regionKrálovéhradecký kraj, regionPardubický kraj, regionJihovýchod, regionKraj Vysočina, regionJihomoravský kraj, regionStřední Morava, regionOlomoucký kraj, regionZlínský kraj, regionMoravskoslezsko, regionMoravskoslezský kraj, regionGermany, regionBaden-Württemberg, regionStuttgart, regionStuttgart, Stadtkreis, regionBöblingen, regionEsslingen, regionGöppingen, regionLudwigsburg, regionRems-Murr-Kreis, regionHeilbronn, Stadtkreis, regionHeilbronn, Landkreis, regionHohenlohekreis, regionSchwäbisch Hall, regionMain-Tauber-Kreis, regionHeidenheim, regionOstalbkreis, regionKarlsruhe, regionBaden-Baden, Stadtkreis, regionKarlsruhe, Stadtkreis, regionKarlsruhe, Landkreis, regionRastatt, regionHeidelberg, Stadtkreis, regionMannheim, Stadtkreis, regionNeckar-Odenwald-Kreis, regionRhein-Neckar-Kreis, regionPforzheim, Stadtkreis, regionCalw, regionEnzkreis, regionFreudenstadt, regionFreiburg, regionFreiburg im Breisgau, Stadtkreis, regionBreisgau-Hochschwarzwald, regionEmmendingen, regionOrtenaukreis, regionRottweil, regionSchwarzwald-Baar-Kreis, regionTuttlingen, regionKonstanz, regionLörrach, regionWaldshut, regionTübingen, regionReutlingen, regionTübingen, Landkreis, regionZollernalbkreis, regionUlm, Stadtkreis, regionAlb-Donau-Kreis, regionBiberach, regionBodenseekreis, regionRavensburg, regionSigmaringen, regionBayern, regionOberbayern, regionIngolstadt, Kreisfreie Stadt, regionMünchen, Kreisfreie Stadt, regionRosenheim, Kreisfreie Stadt, regionAltötting, regionBerchtesgadener Land, regionBad Tölz-Wolfratshausen, regionDachau, regionEbersberg, regionEichstätt, regionErding, regionFreising, regionFürstenfeldbruck, regionGarmisch-Partenkirchen, regionLandsberg am Lech, regionMiesbach, regionMühldorf a. Inn, regionMünchen, Landkreis, regionNeuburg-Schrobenhausen, regionPfaffenhofen a. d. Ilm, regionRosenheim, Landkreis, regionStarnberg, regionTraunstein, regionWeilheim-Schongau, regionNiederbayern, regionLandshut, Kreisfreie Stadt, regionPassau, Kreisfreie Stadt, regionStraubing, Kreisfreie Stadt, regionDeggendorf, regionFreyung-Grafenau, regionKelheim, regionLandshut, Landkreis, regionPassau, Landkreis, regionRegen, regionRottal-Inn, regionStraubing-Bogen, regionDingolfing-Landau, regionOberpfalz, regionAmberg, Kreisfreie Stadt, regionRegensburg, Kreisfreie Stadt, regionWeiden i. d. Opf, Kreisfreie Stadt, regionAmberg-Sulzbach, regionCham, regionNeumarkt i. d. OPf., regionNeustadt a. d. Waldnaab, regionRegensburg, Landkreis, regionSchwandorf, regionTirschenreuth, regionOberfranken, regionBamberg, Kreisfreie Stadt, regionBayreuth, Kreisfreie Stadt, regionCoburg, Kreisfreie Stadt, regionHof, Kreisfreie Stadt, regionBamberg, Landkreis, regionBayreuth, Landkreis, regionCoburg, Landkreis, regionForchheim, regionHof, Landkreis, regionKronach, regionKulmbach, regionLichtenfels, regionWunsiedel i. Fichtelgebirge, regionMittelfranken, regionAnsbach, Kreisfreie Stadt, regionErlangen, Kreisfreie Stadt, regionFürth, Kreisfreie Stadt, regionNürnberg, Kreisfreie Stadt, regionSchwabach, Kreisfreie Stadt, regionAnsbach, Landkreis, regionErlangen-Höchstadt, regionFürth, Landkreis, regionNürnberger Land, regionNeustadt a. d. Aisch-Bad Windsheim, regionRoth, regionWeißenburg-Gunzenhausen, regionUnterfranken, regionAschaffenburg, Kreisfreie Stadt, regionSchweinfurt, Kreisfreie Stadt, regionWürzburg, Kreisfreie Stadt, regionAschaffenburg, Landkreis, regionBad Kissingen, regionRhön-Grabfeld, regionHaßberge, regionKitzingen, regionMiltenberg, regionMain-Spessart, regionSchweinfurt, Landkreis, regionWürzburg, Landkreis, regionSchwaben, regionAugsburg, Kreisfreie Stadt, regionKaufbeuren, Kreisfreie Stadt, regionKempten (Allgäu), Kreisfreie Stadt, regionMemmingen, Kreisfreie Stadt, regionAichach-Friedberg, regionAugsburg, Landkreis, regionDillingen a.d. Donau, regionGünzburg, regionNeu-Ulm, regionLindau (Bodensee), regionOstallgäu, regionUnterallgäu, regionDonau-Ries, regionOberallgäu, regionBerlin, regionBrandenburg, regionBrandenburg an der Havel, Kreisfreie Stadt, regionCottbus, Kreisfreie Stadt, regionFrankfurt (Oder), Kreisfreie Stadt, regionPotsdam, Kreisfreie Stadt, regionBarnim, regionDahme-Spreewald, regionElbe-Elster, regionHavelland, regionMärkisch-Oderland, regionOberhavel, regionOberspreewald-Lausitz, regionOder-Spree, regionOstprignitz-Ruppin, regionPotsdam-Mittelmark, regionPrignitz, regionSpree-Neiße, regionTeltow-Fläming, regionUckermark, regionBremen, regionBremen, Kreisfreie Stadt, regionBremerhaven, Kreisfreie Stadt, regionHamburg, regionHessen, regionDarmstadt, regionDarmstadt, Kreisfreie Stadt, regionFrankfurt am Main, Kreisfreie Stadt, regionOffenbach am Main, Kreisfreie Stadt, regionWiesbaden, Kreisfreie Stadt, regionBergstraße, regionDarmstadt-Dieburg, regionGroß-Gerau, regionHochtaunuskreis, regionMain-Kinzig-Kreis, regionMain-Taunus-Kreis, regionOdenwaldkreis, regionOffenbach, Landkreis, regionRheingau-Taunus-Kreis, regionWetteraukreis, regionGießen, regionGießen, Landkreis, regionLahn-Dill-Kreis, regionLimburg-Weilburg, regionMarburg-Biedenkopf, regionVogelsbergkreis, regionKassel, regionKassel, Kreisfreie Stadt, regionFulda, regionHersfeld-Rotenburg, regionKassel, Landkreis, regionSchwalm-Eder-Kreis, regionWaldeck-Frankenberg, regionWerra-Meißner-Kreis, regionMecklenburg-Vorpommern, regionRostock, Kreisfreie Stadt, regionSchwerin, Kreisfreie Stadt, regionMecklenburgische Seenplatte, regionLandkreis Rostock, regionVorpommern-Rügen, regionNordwestmecklenburg, regionVorpommern-Greifswald, regionLudwigslust-Parchim, regionNiedersachsen, regionBraunschweig, regionBraunschweig, Kreisfreie Stadt, regionSalzgitter, Kreisfreie Stadt, regionWolfsburg, Kreisfreie Stadt, regionGifhorn, regionGoslar, regionHelmstedt, regionNortheim, regionPeine, regionWolfenbüttel, regionGöttingen, 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regionПчињска област, regionТопличка област, regionRussian Federation, regionNorth and North West, regionCenter, regionVolgo-Vyatsky, regionCentral-Chernozhem, regionVolga, regionNorth Caucasus, regionUral, regionWest Siberia, regionEast Siberia, regionFar East, regionCentral FO, regionNorth-Western FO, regionVolga FO, regionSouth FO, regionNorth Caucasian FO, regionUral FO, regionSiberian FO, regionFar East FO, regionSweden, regionÖstra Sverige, regionStockholm, regionStockholms län, regionÖstra Mellansverige, regionUppsala län, regionSödermanlands län, regionÖstergötlands län, regionÖrebro län, regionVästmanlands län, regionSödra Sverige, regionSmåland med öarna, regionJönköpings län, regionKronobergs län, regionKalmar län, regionGotlands län, regionSydsverige, regionBlekinge län, regionSkåne län, regionVästsverige, regionHallands län, regionVästra Götalands län, regionNorra Sverige, regionNorra Mellansverige, regionVärmlands län, regionDalarnas län, regionGävleborgs län, regionMellersta Norrland, regionVästernorrlands län, regionJämtlands län, regionÖvre Norrland, regionVästerbottens län, regionNorrbottens län, regionSlovenia, regionSlovenija, regionPomurska, regionPodravska, regionKoroška, regionSavinjska, regionZasavska, regionSpodnjeposavska, regionJugovzhodna Slovenija, regionNotranjsko-kraška, regionOsrednjeslovenska, regionGorenjska, regionGoriška, regionObalno-kraška, regionVzhodna Slovenija, regionPosavska, regionPrimorsko-notranjska, regionZahodna Slovenija, regionSlovakia, regionSlovensko, regionBratislavský kraj, regionZápadné Slovensko, regionTrnavský kraj, regionTrenčiansky kraj, regionNitriansky kraj, regionStredné Slovensko, regionŽilinský kraj, regionBanskobystrický kraj, regionVýchodné Slovensko, regionPrešovský kraj, regionKošický kraj, regionTurkey, regionİstanbul, regionBatı Marmara, regionTekirdağ, Edirne, Kırklareli, regionTekirdağ, regionEdirne, regionKırklareli, regionBalıkesir, Çanakkale, regionBalıkesir, regionÇanakkale, 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Bartın, regionZonguldak, regionKarabük, regionBartın, regionKastamonu, Çankırı, Sinop, regionKastamonu, regionÇankırı, regionSinop, regionSamsun, Tokat, Çorum, Amasya, regionSamsun, regionTokat, regionÇorum, regionAmasya, regionDoğu Karadeniz, regionTrabzon, Ordu, Giresun, Rize, Artvin, Gümüşhane, regionTrabzon, regionOrdu, regionGiresun, regionRize, regionArtvin, regionGümüşhane, regionKuzeydoğu Anadolu, regionErzurum, Erzincan, Bayburt, regionErzurum, regionErzincan, regionBayburt, regionAğrı, Kars, Iğdır, Ardahan, regionAğrı, regionKars, regionIğdır, regionArdahan, regionOrtadoğu Anadolu, regionMalatya, Elazığ, Bingöl, Tunceli, regionMalatya, regionElazığ, regionBingöl, regionTunceli, regionVan, Muş, Bitlis, Hakkari, regionVan, regionMuş, regionBitlis, regionHakkari, regionGüneydoğu Anadolu, regionGaziantep, Adıyaman, Kilis, regionGaziantep, regionAdıyaman, regionKilis, regionŞanlıurfa, Diyarbakır, regionŞanlıurfa, regionDiyarbakır, regionMardin, Batman, Şırnak, Siirt, regionMardin, regionBatman, regionŞırnak, regionSiirt, regionUkraine, regionCrimea, Autonomy Republic, regionVynnytska oblast, regionVolynska oblast, regionDnipropetrovska oblast, regionDonetska oblast, regionZhytomyrska oblast, regionZakarpatska oblast, regionZaporizska oblast, regionIvano-Frankivska oblast, regionKyivska oblast, regionKirovogradska oblast, regionLuganska oblast, regionLvivska oblast, regionMykolaivska oblast, regionOdesska oblast, regionPoltavska oblast, regionRivenska oblast, regionSumska oblast, regionTernopilska oblast, regionKharkivska oblast, regionKhersonska oblast, regionKhmelnitska oblast, regionCherkasska oblast, regionChernovytska oblast, regionChernigivska oblast, regionKyiv city, regionTees Valley and Durham, regionHartlepool and Stockton-on-Tees, regionSouth Teesside, regionDarlington, regionDurham CC, regionNorthumberland and Tyne and Wear, regionNorthumberland, regionTyneside, regionSunderland, regionCumbria, regionWest Cumbria, regionEast Cumbria, regionGreater Manchester, regionManchester, regionGreater Manchester South West, regionGreater Manchester South East, regionGreater Manchester North West, regionGreater Manchester North East, regionLancashire, regionBlackburn with Darwen, regionBlackpool, regionLancaster and Wyre, regionMid Lancashire, regionEast Lancashire, regionChorley and West Lancashire, regionCheshire, regionWarrington, regionCheshire East, regionCheshire West and Chester, regionMerseyside, regionEast Merseyside, regionLiverpool, regionSefton, regionWirral, regionEast Yorkshire and Northern Lincolnshire, regionKingston upon Hull, City of, regionEast Riding of Yorkshire, regionNorth and North East Lincolnshire, regionNorth Yorkshire, regionYork, regionNorth Yorkshire CC, regionSouth Yorkshire, regionBarnsley, Doncaster and Rotherham, regionSheffield, regionWest Yorkshire, regionBradford, regionLeeds, regionCalderdale and Kirklees, regionWakefield, regionDerbyshire and Nottinghamshire, regionDerby, regionEast Derbyshire, 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regionSouthend-on-Sea, regionThurrock, regionEssex Haven Gateway, regionWest Essex, regionHeart of Essex, regionEssex Thames Gateway, regionInner London — West, regionCamden and City of London, regionWestminster, regionKensington & Chelsea and Hammersmith & Fulham, regionWandsworth, regionInner London — East, regionHackney and Newham, regionTower Hamlets, regionHaringey and Islington, regionLewisham and Southwark, regionLambeth, regionOuter London — East and North East, regionBexley and Greenwich, regionBarking & Dagenham and Havering, regionRedbridge and Waltham Forest, regionEnfield, regionOuter London — South, regionBromley, regionCroydon, regionMerton, Kingston upon Thames and Sutton, regionOuter London — West and North West, regionBarnet, regionBrent, regionEaling, regionHarrow and Hillingdon, regionHounslow and Richmond upon Thames, regionSouth East (England), regionBerkshire, Buckinghamshire and Oxfordshire, regionBerkshire, regionMilton Keynes, regionBuckinghamshire CC, regionOxfordshire, regionSurrey, East and West Sussex, regionBrighton and Hove, regionEast Sussex CC, regionWest Surrey, regionEast Surrey, regionWest Sussex (South West), regionWest Sussex (North East), regionHampshire and Isle of Wight, regionPortsmouth, regionSouthampton, regionIsle of Wight, regionSouth Hampshire, regionCentral Hampshire, regionNorth Hampshire, regionKent, regionMedway, regionKent Thames Gateway, regionEast Kent, regionMid Kent, regionWest Kent, regionGloucestershire, Wiltshire and Bristol/Bath area, regionBristol, City of, regionBath and North East Somerset, North Somerset and South Gloucestershire, regionGloucestershire, regionSwindon, regionWiltshire CC, regionDorset and Somerset, regionSomerset, regionBournemouth, Christchurch and Poole, regionDorset, regionCornwall and Isles of Scilly, regionDevon, regionPlymouth, regionTorbay, regionDevon CC, regionWest Wales and The Valleys, regionIsle of Anglesey, regionGwynedd, regionConwy and Denbighshire, regionSouth West Wales, regionCentral Valleys, regionGwent Valleys, regionBridgend and Neath Port Talbot, regionSwansea, regionEast Wales, regionMonmouthshire and Newport, regionCardiff and Vale of Glamorgan, regionFlintshire and Wrexham, regionPowys, regionNorth Eastern Scotland, regionAberdeen City and Aberdeenshire, regionHighlands and Islands, regionCaithness & Sutherland and Ross & Cromarty, regionInverness & Nairn and Moray, Badenoch & Strathspey, regionLochaber, Skye & Lochalsh, Arran & Cumbrae and Argyll & Bute, regionNa h-Eileanan Siar (Western Isles), regionOrkney Islands, regionShetland Islands, regionEastern Scotland, regionAngus and Dundee City, regionClackmannanshire and Fife, regionEast Lothian and Midlothian, regionEdinburgh, City of, regionFalkirk, regionPerth & Kinross and Stirling, regionWest Lothian, regionWest Central Scotland, regionEast Dunbartonshire, West Dunbartonshire and Helensburgh & Lomond, regionGlasgow City, regionInverclyde, East Renfrewshire and Renfrewshire, regionNorth Lanarkshire, regionSouthern Scotland, regionScottish Borders, regionDumfries & Galloway, regionEast Ayrshire and North Ayrshire mainland, regionSouth Ayrshire, regionSouth Lanarkshire, regionBelfast, regionArmagh City, Banbridge and Craigavon, regionNewry, Mourne and Down, regionArds and North Down, regionDerry City and Strabane, regionMid Ulster, regionCauseway Coast and Glens, regionAntrim and Newtownabbey, regionLisburn and Castlereagh, regionMid and East Antrim, regionFermanagh and Omagh, regionKosovo, regionPrishtinë, regionMitrovicë, regionPejë, regionPrizren, regionFerizaj, regionGjilan, regionGjakovë
## 5 genderNo answer, income_stressRefusal, income_stressDon't know, income_stressNo answer, regionAlbania, regionShqipëria, regionVeri, regionDibër, regionDurrës, regionKukës, regionLezhë, regionShkodër, regionQender, regionElbasan, regionTiranë, regionJug, regionBerat, regionFier, regionGjirokastër, regionKorcë, regionVlorë, regionExtra-Regio NUTS 1, regionExtra-Regio NUTS 2, regionExtra-Regio NUTS 3, regionAustria, regionOstösterreich, regionBurgenland, regionMittelburgenland, regionNordburgenland, regionSüdburgenland, regionNiederösterreich, regionMostviertel-Eisenwurzen, regionNiederösterreich-Süd, regionSankt Pölten, regionWaldviertel, regionWeinviertel, regionWiener Umland/Nordteil, regionWiener Umland/Südteil, regionWien, regionSüdösterreich, regionKärnten, regionKlagenfurt-Villach, regionOberkärnten, regionUnterkärnten, regionSteiermark, regionGraz, regionLiezen, regionÖstliche Obersteiermark, regionOststeiermark, regionWest- und Südsteiermark, regionWestliche Obersteiermark, regionWestösterreich, regionOberösterreich, regionInnviertel, regionLinz-Wels, regionMühlviertel, regionSteyr-Kirchdorf, regionTraunviertel, regionSalzburg, regionLungau, regionPinzgau-Pongau, regionSalzburg und Umgebung, regionTirol, regionAußerfern, regionInnsbruck, regionOsttirol, regionTiroler Oberland, regionTiroler Unterland, regionVorarlberg, regionBludenz-Bregenzer Wald, regionRheintal-Bodenseegebiet, regionBelgium, regionRégion de Bruxelles-Capitale/Brussels Hoofdstedelijk Gewest, regionRégion de Bruxelles-Capitale/ Brussels Hoofdstedelijk Gewest, regionArr. de Bruxelles-Capitale/Arr. Brussel-Hoofdstad, regionVlaams Gewest, regionProv. Antwerpen, regionArr. Antwerpen, regionArr. Mechelen, regionArr. Turnhout, regionProv. Limburg (BE), regionArr. Tongeren, regionArr. Hasselt, regionArr. Maaseik, regionProv. Oost-Vlaanderen, regionArr. Aalst, regionArr. Dendermonde, regionArr. Eeklo, regionArr. Gent, regionArr. Oudenaarde, regionArr. Sint-Niklaas, regionProv. Vlaams-Brabant, regionArr. Halle-Vilvoorde, regionArr. Leuven, regionProv. West-Vlaanderen, regionArr. Brugge, regionArr. Diksmuide, regionArr. Ieper, regionArr. Kortrijk, regionArr. Oostende, regionArr. Roeselare, regionArr. Tielt, regionArr. Veurne, regionRégion wallonne, regionProv. Brabant Wallon, regionArr. Nivelles, regionProv. Hainaut, regionArr. Mons, regionArr. Tournai-Mouscron, regionArr. La Louvière, regionArr. Ath, regionArr. Charleroi, regionArr. Soignies, regionArr. Thuin, regionProv. Liège, regionArr. Huy, regionArr. Liège, regionArr. Waremme, regionArr. Verviers — communes francophones, regionBezirk Verviers — Deutschsprachige Gemeinschaft, regionProv. Luxembourg (BE), regionArr. Arlon, regionArr. Bastogne, regionArr. Marche-en-Famenne, regionArr. Neufchâteau, regionArr. Virton, regionProv. Namur, regionArr. Dinant, regionArr. Namur, regionArr. Philippeville, regionBulgaria, regionСеверна и Югоизточна България, regionСеверозападен, regionВидин, regionМонтана, regionВраца, regionПлевен, regionЛовеч, regionСеверен централен, regionВелико Търново, regionГаброво, regionРусе, regionРазград, regionСилистра, regionСевероизточен, regionВарна, regionДобрич, regionШумен, regionТърговище, regionЮгоизточен, regionБургас, regionСливен, regionЯмбол, regionСтара Загора, regionЮгозападна и Южна централна България, regionЮгозападен, regionСофия (столица), regionСофия, regionБлагоевград, regionПерник, regionКюстендил, regionЮжен централен, regionПловдив, regionХасково, regionПазарджик, regionСмолян, regionКърджали, regionSwitzerland, regionSchweiz/Suisse/Svizzera, regionRégion lémanique, regionVaud, regionValais / Wallis, regionGenève, regionEspace Mittelland, regionBern / Berne, regionFribourg / Freiburg, regionSolothurn, regionNeuchâtel, regionJura, regionNordwestschweiz, regionBasel-Stadt, regionBasel-Landschaft, regionAargau, regionZürich, regionOstschweiz, regionGlarus, regionSchaffhausen, regionAppenzell Ausserrhoden, regionAppenzell Innerrhoden, regionSt. Gallen, regionGraubünden / Grigioni / Grischun, regionThurgau, regionZentralschweiz, regionLuzern, regionUri, regionSchwyz, regionObwalden, regionNidwalden, regionZug, regionTicino, regionCyprus, regionΚύπρος, regionCzechia, regionČesko, regionPraha, regionHlavní město Praha, regionStřední Čechy, regionStředočeský kraj, regionJihozápad, regionJihočeský kraj, regionPlzeňský kraj, regionSeverozápad, regionKarlovarský kraj, regionÚstecký kraj, regionSeverovýchod, regionLiberecký kraj, regionKrálovéhradecký kraj, regionPardubický kraj, regionJihovýchod, regionKraj Vysočina, regionJihomoravský kraj, regionStřední Morava, regionOlomoucký kraj, regionZlínský kraj, regionMoravskoslezsko, regionMoravskoslezský kraj, regionGermany, regionBaden-Württemberg, regionStuttgart, regionStuttgart, Stadtkreis, regionBöblingen, regionEsslingen, regionGöppingen, regionLudwigsburg, regionRems-Murr-Kreis, regionHeilbronn, Stadtkreis, regionHeilbronn, Landkreis, regionHohenlohekreis, regionSchwäbisch Hall, regionMain-Tauber-Kreis, regionHeidenheim, regionOstalbkreis, regionKarlsruhe, regionBaden-Baden, Stadtkreis, regionKarlsruhe, Stadtkreis, regionKarlsruhe, Landkreis, regionRastatt, regionHeidelberg, Stadtkreis, regionMannheim, Stadtkreis, regionNeckar-Odenwald-Kreis, regionRhein-Neckar-Kreis, regionPforzheim, Stadtkreis, regionCalw, regionEnzkreis, regionFreudenstadt, regionFreiburg, regionFreiburg im Breisgau, Stadtkreis, regionBreisgau-Hochschwarzwald, regionEmmendingen, regionOrtenaukreis, regionRottweil, regionSchwarzwald-Baar-Kreis, regionTuttlingen, regionKonstanz, regionLörrach, regionWaldshut, regionTübingen, regionReutlingen, regionTübingen, Landkreis, regionZollernalbkreis, regionUlm, Stadtkreis, regionAlb-Donau-Kreis, regionBiberach, regionBodenseekreis, regionRavensburg, regionSigmaringen, regionBayern, regionOberbayern, regionIngolstadt, Kreisfreie Stadt, regionMünchen, Kreisfreie Stadt, regionRosenheim, Kreisfreie Stadt, regionAltötting, regionBerchtesgadener Land, regionBad Tölz-Wolfratshausen, regionDachau, regionEbersberg, regionEichstätt, regionErding, regionFreising, regionFürstenfeldbruck, regionGarmisch-Partenkirchen, regionLandsberg am Lech, regionMiesbach, regionMühldorf a. Inn, regionMünchen, Landkreis, regionNeuburg-Schrobenhausen, regionPfaffenhofen a. d. Ilm, regionRosenheim, Landkreis, regionStarnberg, regionTraunstein, regionWeilheim-Schongau, regionNiederbayern, regionLandshut, Kreisfreie Stadt, regionPassau, Kreisfreie Stadt, regionStraubing, Kreisfreie Stadt, regionDeggendorf, regionFreyung-Grafenau, regionKelheim, regionLandshut, Landkreis, regionPassau, Landkreis, regionRegen, regionRottal-Inn, regionStraubing-Bogen, regionDingolfing-Landau, regionOberpfalz, regionAmberg, Kreisfreie Stadt, regionRegensburg, Kreisfreie Stadt, regionWeiden i. d. Opf, Kreisfreie Stadt, regionAmberg-Sulzbach, regionCham, regionNeumarkt i. d. OPf., regionNeustadt a. d. 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## 6 genderNo answer, income_stressRefusal, income_stressDon't know, income_stressNo answer, area_typeRefusal, area_typeDon't know, area_typeNo answer, regionAlbania, regionShqipëria, regionVeri, regionDibër, regionDurrës, regionKukës, regionLezhë, regionShkodër, regionQender, regionElbasan, regionTiranë, regionJug, regionBerat, regionFier, regionGjirokastër, regionKorcë, regionVlorë, regionExtra-Regio NUTS 1, regionExtra-Regio NUTS 2, regionExtra-Regio NUTS 3, regionAustria, regionOstösterreich, regionBurgenland, regionMittelburgenland, regionNordburgenland, regionSüdburgenland, regionNiederösterreich, regionMostviertel-Eisenwurzen, regionNiederösterreich-Süd, regionSankt Pölten, regionWaldviertel, regionWeinviertel, regionWiener Umland/Nordteil, regionWiener Umland/Südteil, regionWien, regionSüdösterreich, regionKärnten, regionKlagenfurt-Villach, regionOberkärnten, regionUnterkärnten, regionSteiermark, regionGraz, regionLiezen, regionÖstliche Obersteiermark, regionOststeiermark, regionWest- und Südsteiermark, regionWestliche Obersteiermark, regionWestösterreich, regionOberösterreich, regionInnviertel, regionLinz-Wels, regionMühlviertel, regionSteyr-Kirchdorf, regionTraunviertel, regionSalzburg, regionLungau, regionPinzgau-Pongau, regionSalzburg und Umgebung, regionTirol, regionAußerfern, regionInnsbruck, regionOsttirol, regionTiroler Oberland, regionTiroler Unterland, regionVorarlberg, regionBludenz-Bregenzer Wald, regionRheintal-Bodenseegebiet, regionBelgium, regionRégion de Bruxelles-Capitale/Brussels Hoofdstedelijk Gewest, regionRégion de Bruxelles-Capitale/ Brussels Hoofdstedelijk Gewest, regionArr. de Bruxelles-Capitale/Arr. 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Verviers — communes francophones, regionBezirk Verviers — Deutschsprachige Gemeinschaft, regionProv. Luxembourg (BE), regionArr. Arlon, regionArr. Bastogne, regionArr. Marche-en-Famenne, regionArr. Neufchâteau, regionArr. Virton, regionProv. Namur, regionArr. Dinant, regionArr. Namur, regionArr. 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regionRegião de Coimbra, regionRegião de Leiria, regionViseu Dão Lafões, regionBeira Baixa, regionMédio Tejo, regionBeiras e Serra da Estrela, regionÁrea Metropolitana de Lisboa, regionAlentejo, regionAlentejo Litoral, regionBaixo Alentejo, regionLezíria do Tejo, regionAlto Alentejo, regionAlentejo Central, regionRegião Autónoma dos Açores, regionRegião Autónoma da Madeira, regionRomania, regionMacroregiunea Unu, regionNord-Vest, regionBihor, regionBistriţa-Năsăud, regionCluj, regionMaramureş, regionSatu Mare, regionSălaj, regionCentru, regionAlba, regionBraşov, regionCovasna, regionHarghita, regionMureş, regionSibiu, regionMacroregiunea Doi, regionBacău, regionBotoşani, regionIaşi, regionNeamţ, regionSuceava, regionVaslui, regionSud-Est, regionBrăila, regionBuzău, regionConstanţa, regionGalaţi, regionTulcea, regionVrancea, regionMacroregiunea Trei, regionSud-Muntenia, regionArgeş, regionCălăraşi, regionDâmboviţa, regionGiurgiu, regionIalomiţa, regionPrahova, regionTeleorman, regionBucureşti-Ilfov, regionBucureşti, region Ilfov, regionMacroregiunea Patru, regionSud-Vest Oltenia, regionDolj, regionGorj, regionMehedinţi, regionOlt, regionVâlcea, regionVest, regionArad, regionCaraş-Severin, regionHunedoara, regionTimiş, regionSerbia, regionСрбија - север, regionБеоградски регион, regionБеоградска област, regionРегион Војводине, regionЗападнобачка област, regionЈужнобанатска област, regionЈужнобачка област, regionСевернобанатска област, regionСевернобачка област, regionСредњобанатска област, regionСремска област, regionСрбија - југ, regionРегион Шумадије и Западне Србије, regionЗлатиборска област, regionКолубарска област, regionМачванска област, regionМоравичка област, regionПоморавска област, regionРасинска област, regionРашка област, regionШумадијска област, regionРегион Јужне и Источне Србије, regionБорска област, regionБраничевска област, regionЗајечарска област, regionЈабланичка област, regionНишавска област, regionПиротска област, regionПодунавска област, regionПчињска област, regionТопличка област, regionRussian Federation, regionNorth and North West, regionCenter, regionVolgo-Vyatsky, regionCentral-Chernozhem, regionVolga, regionNorth Caucasus, regionUral, regionWest Siberia, regionEast Siberia, regionFar East, regionCentral FO, regionNorth-Western FO, regionVolga FO, regionSouth FO, regionNorth Caucasian FO, regionUral FO, regionSiberian FO, regionFar East FO, regionSweden, regionÖstra Sverige, regionStockholm, regionStockholms län, regionÖstra Mellansverige, regionUppsala län, regionSödermanlands län, regionÖstergötlands län, regionÖrebro län, regionVästmanlands län, regionSödra Sverige, regionSmåland med öarna, regionJönköpings län, regionKronobergs län, regionKalmar län, regionGotlands län, regionSydsverige, regionBlekinge län, regionSkåne län, regionVästsverige, regionHallands län, regionVästra Götalands län, regionNorra Sverige, regionNorra Mellansverige, regionVärmlands län, regionDalarnas län, regionGävleborgs län, regionMellersta Norrland, regionVästernorrlands län, regionJämtlands län, regionÖvre Norrland, regionVästerbottens län, regionNorrbottens län, regionSlovenia, regionSlovenija, regionPomurska, regionPodravska, regionKoroška, regionSavinjska, regionZasavska, regionSpodnjeposavska, regionJugovzhodna Slovenija, regionNotranjsko-kraška, regionOsrednjeslovenska, regionGorenjska, regionGoriška, regionObalno-kraška, regionVzhodna Slovenija, regionPosavska, regionPrimorsko-notranjska, regionZahodna Slovenija, regionSlovakia, regionSlovensko, regionBratislavský kraj, regionZápadné Slovensko, regionTrnavský kraj, regionTrenčiansky kraj, regionNitriansky kraj, regionStredné Slovensko, regionŽilinský kraj, regionBanskobystrický kraj, regionVýchodné Slovensko, regionPrešovský kraj, regionKošický kraj, regionTurkey, regionİstanbul, regionBatı Marmara, regionTekirdağ, Edirne, Kırklareli, regionTekirdağ, regionEdirne, regionKırklareli, regionBalıkesir, Çanakkale, regionBalıkesir, regionÇanakkale, regionEge, regionİzmir, regionAydın, Denizli, Muğla, regionAydın, regionDenizli, regionMuğla, regionManisa, Afyonkarahisar, Kütahya, Uşak, regionManisa, regionAfyonkarahisar, regionKütahya, regionUşak, regionDoğu Marmara, regionBursa, Eskişehir, Bilecik, regionBursa, regionEskişehir, regionBilecik, regionKocaeli, Sakarya, Düzce, Bolu, Yalova, regionKocaeli, regionSakarya, regionDüzce, regionBolu, regionYalova, regionBatı Anadolu, regionAnkara, regionKonya, Karaman, regionKonya, regionKaraman, regionAkdeniz, regionAntalya, Isparta, Burdur, regionAntalya, regionIsparta, regionBurdur, regionAdana, Mersin, regionAdana, regionMersin, regionHatay, Kahramanmaraş, Osmaniye, regionHatay, regionKahramanmaraş, regionOsmaniye, regionOrta Anadolu, regionKırıkkale, Aksaray, Niğde, Nevşehir, Kırşehir, regionKırıkkale, regionAksaray, regionNiğde, regionNevşehir, regionKırşehir, regionKayseri, Sivas, Yozgat, regionKayseri, regionSivas, regionYozgat, regionBatı Karadeniz, regionZonguldak, Karabük, Bartın, regionZonguldak, regionKarabük, regionBartın, regionKastamonu, Çankırı, Sinop, regionKastamonu, regionÇankırı, regionSinop, regionSamsun, Tokat, Çorum, Amasya, regionSamsun, regionTokat, regionÇorum, regionAmasya, regionDoğu Karadeniz, regionTrabzon, Ordu, Giresun, Rize, Artvin, Gümüşhane, regionTrabzon, regionOrdu, regionGiresun, regionRize, regionArtvin, regionGümüşhane, regionKuzeydoğu Anadolu, regionErzurum, Erzincan, Bayburt, regionErzurum, regionErzincan, regionBayburt, regionAğrı, Kars, Iğdır, Ardahan, regionAğrı, regionKars, regionIğdır, regionArdahan, regionOrtadoğu Anadolu, regionMalatya, Elazığ, Bingöl, Tunceli, regionMalatya, regionElazığ, regionBingöl, regionTunceli, regionVan, Muş, Bitlis, Hakkari, regionVan, regionMuş, regionBitlis, regionHakkari, regionGüneydoğu Anadolu, regionGaziantep, Adıyaman, Kilis, regionGaziantep, regionAdıyaman, regionKilis, regionŞanlıurfa, Diyarbakır, regionŞanlıurfa, regionDiyarbakır, regionMardin, Batman, Şırnak, Siirt, regionMardin, regionBatman, regionŞırnak, regionSiirt, regionUkraine, regionCrimea, Autonomy Republic, regionVynnytska oblast, regionVolynska oblast, regionDnipropetrovska oblast, regionDonetska oblast, regionZhytomyrska oblast, regionZakarpatska oblast, regionZaporizska oblast, regionIvano-Frankivska oblast, regionKyivska oblast, regionKirovogradska oblast, regionLuganska oblast, regionLvivska oblast, regionMykolaivska oblast, regionOdesska oblast, regionPoltavska oblast, regionRivenska oblast, regionSumska oblast, regionTernopilska oblast, regionKharkivska oblast, regionKhersonska oblast, regionKhmelnitska oblast, regionCherkasska oblast, regionChernovytska oblast, regionChernigivska oblast, regionKyiv city, regionTees Valley and Durham, regionHartlepool and Stockton-on-Tees, regionSouth Teesside, regionDarlington, regionDurham CC, regionNorthumberland and Tyne and Wear, regionNorthumberland, regionTyneside, regionSunderland, regionCumbria, regionWest Cumbria, regionEast Cumbria, regionGreater Manchester, regionManchester, regionGreater Manchester South West, regionGreater Manchester South East, regionGreater Manchester North West, regionGreater Manchester North East, regionLancashire, regionBlackburn with Darwen, regionBlackpool, regionLancaster and Wyre, regionMid Lancashire, regionEast Lancashire, regionChorley and West Lancashire, regionCheshire, regionWarrington, regionCheshire East, regionCheshire West and Chester, regionMerseyside, regionEast Merseyside, regionLiverpool, regionSefton, regionWirral, regionEast Yorkshire and Northern Lincolnshire, regionKingston upon Hull, City of, regionEast Riding of Yorkshire, regionNorth and North East Lincolnshire, regionNorth Yorkshire, regionYork, regionNorth Yorkshire CC, regionSouth Yorkshire, regionBarnsley, Doncaster and Rotherham, regionSheffield, regionWest Yorkshire, regionBradford, regionLeeds, regionCalderdale and Kirklees, regionWakefield, regionDerbyshire and Nottinghamshire, regionDerby, regionEast Derbyshire, regionSouth and West Derbyshire, regionNottingham, regionNorth Nottinghamshire, regionSouth Nottinghamshire, regionLeicestershire, Rutland and Northamptonshire, regionLeicester, regionLeicestershire CC and Rutland, regionWest Northamptonshire, regionNorth Northamptonshire, regionLincolnshire, regionHerefordshire, Worcestershire and Warwickshire, regionHerefordshire, County of, regionWorcestershire, regionWarwickshire, regionShropshire and Staffordshire, regionTelford and Wrekin, regionShropshire CC, regionStoke-on-Trent, regionStaffordshire CC, regionWest Midlands, regionBirmingham, regionSolihull, regionCoventry, regionDudley, regionSandwell, regionWalsall, regionWolverhampton, regionEast Anglia, regionPeterborough, regionCambridgeshire CC, regionSuffolk, regionNorwich and East Norfolk, regionNorth and West Norfolk, regionBreckland and South Norfolk, regionBedfordshire and Hertfordshire, regionLuton, regionHertfordshire, regionBedford, regionCentral Bedfordshire, regionEssex, regionSouthend-on-Sea, regionThurrock, regionEssex Haven Gateway, regionWest Essex, regionHeart of Essex, regionEssex Thames Gateway, regionInner London — West, regionCamden and City of London, regionWestminster, regionKensington & Chelsea and Hammersmith & Fulham, regionWandsworth, regionInner London — East, regionHackney and Newham, regionTower Hamlets, regionHaringey and Islington, regionLewisham and Southwark, regionLambeth, regionOuter London — East and North East, regionBexley and Greenwich, regionBarking & Dagenham and Havering, regionRedbridge and Waltham Forest, regionEnfield, regionOuter London — South, regionBromley, regionCroydon, regionMerton, Kingston upon Thames and Sutton, regionOuter London — West and North West, regionBarnet, regionBrent, regionEaling, regionHarrow and Hillingdon, regionHounslow and Richmond upon Thames, regionSouth East (England), regionBerkshire, Buckinghamshire and Oxfordshire, regionBerkshire, regionMilton Keynes, regionBuckinghamshire CC, regionOxfordshire, regionSurrey, East and West Sussex, regionBrighton and Hove, regionEast Sussex CC, regionWest Surrey, regionEast Surrey, regionWest Sussex (South West), regionWest Sussex (North East), regionHampshire and Isle of Wight, regionPortsmouth, regionSouthampton, regionIsle of Wight, regionSouth Hampshire, regionCentral Hampshire, regionNorth Hampshire, regionKent, regionMedway, regionKent Thames Gateway, regionEast Kent, regionMid Kent, regionWest Kent, regionGloucestershire, Wiltshire and Bristol/Bath area, regionBristol, City of, regionBath and North East Somerset, North Somerset and South Gloucestershire, regionGloucestershire, regionSwindon, regionWiltshire CC, regionDorset and Somerset, regionSomerset, regionBournemouth, Christchurch and Poole, regionDorset, regionCornwall and Isles of Scilly, regionDevon, regionPlymouth, regionTorbay, regionDevon CC, regionWest Wales and The Valleys, regionIsle of Anglesey, regionGwynedd, regionConwy and Denbighshire, regionSouth West Wales, regionCentral Valleys, regionGwent Valleys, regionBridgend and Neath Port Talbot, regionSwansea, regionEast Wales, regionMonmouthshire and Newport, regionCardiff and Vale of Glamorgan, regionFlintshire and Wrexham, regionPowys, regionNorth Eastern Scotland, regionAberdeen City and Aberdeenshire, regionHighlands and Islands, regionCaithness & Sutherland and Ross & Cromarty, regionInverness & Nairn and Moray, Badenoch & Strathspey, regionLochaber, Skye & Lochalsh, Arran & Cumbrae and Argyll & Bute, regionNa h-Eileanan Siar (Western Isles), regionOrkney Islands, regionShetland Islands, regionEastern Scotland, regionAngus and Dundee City, regionClackmannanshire and Fife, regionEast Lothian and Midlothian, regionEdinburgh, City of, regionFalkirk, regionPerth & Kinross and Stirling, regionWest Lothian, regionWest Central Scotland, regionEast Dunbartonshire, West Dunbartonshire and Helensburgh & Lomond, regionGlasgow City, regionInverclyde, East Renfrewshire and Renfrewshire, regionNorth Lanarkshire, regionSouthern Scotland, regionScottish Borders, regionDumfries & Galloway, regionEast Ayrshire and North Ayrshire mainland, regionSouth Ayrshire, regionSouth Lanarkshire, regionBelfast, regionArmagh City, Banbridge and Craigavon, regionNewry, Mourne and Down, regionArds and North Down, regionDerry City and Strabane, regionMid Ulster, regionCauseway Coast and Glens, regionAntrim and Newtownabbey, regionLisburn and Castlereagh, regionMid and East Antrim, regionFermanagh and Omagh, regionKosovo, regionPrishtinë, regionMitrovicë, regionPejë, regionPrizren, regionFerizaj, regionGjilan, regionGjakovëNow join together the final datasets and check for any remaining NA’s.
# join all the imputations and check we don't have any NA's
final_data <- complete(imputed_data)
colSums(is.na(final_data))## essround gender age_rec educ_level
## 0 0 0 0
## income_group income_stress area_type region
## 0 0 0 0
## health_disability left_right polintr_binary life_sat
## 0 0 0 0
## econ_sat immig_support daily_netuse country_attach
## 0 0 0 0
## post_polonline
## 0Now join back our target variables and weights. We just join the target variables:
Importantly, we drop the NA values
final_data_regress <- final_data |>
cbind(gb_clean_regress$anweight) |>
cbind(gb_clean_regress$trust_europ) |>
cbind(gb_clean_regress$target_diff) |>
cbind(gb_clean_regress$target_ratiolog) |>
rename("anweight" = "gb_clean_regress$anweight",
"trust_europ" = "gb_clean_regress$trust_europ",
"target_diff" = "gb_clean_regress$target_diff",
"target_ratiolog" = "gb_clean_regress$target_ratiolog") |>
relocate(c(essround, anweight, trust_europ, target_diff, target_ratiolog))
summary(final_data_regress)## essround anweight trust_europ target_diff
## 8 :1959 Min. : 0.8707 Min. : 0.000 Min. :-10.0000
## 9 :2204 1st Qu.: 1.7623 1st Qu.: 1.000 1st Qu.: -2.0000
## 10:1149 Median : 2.5971 Median : 4.000 Median : 0.0000
## 11:1684 Mean : 3.1341 Mean : 3.582 Mean : -0.6205
## 3rd Qu.: 3.8345 3rd Qu.: 5.000 3rd Qu.: 1.0000
## Max. :19.1769 Max. :10.000 Max. : 10.0000
## NA's :502 NA's :529
## target_ratiolog gender age_rec educ_level
## Min. :-4.6052 Male :3200 <20 : 191 Lower secondary:2154
## 1st Qu.:-0.5878 Female :3796 20-29: 670 Upper secondary:1579
## Median : 0.0000 No answer: 0 30-44:1632 Tertiary :3263
## Mean :-0.3404 45-64:2294
## 3rd Qu.: 0.1335 >=65 :2209
## Max. : 4.6052
## NA's :529
## income_group income_stress
## High (Decile 8+) :1840 Living comfortably on present income:3147
## Low (Decile <4) :2622 Coping on present income :2859
## Moderate (Decile 4-7):2534 Difficult on present income : 781
## Very difficult on present income : 209
## Refusal : 0
## Don't know : 0
## No answer : 0
## area_type region
## A big city : 763 South East (England): 947
## Suburbs or outskirts of big city:1435 North West (England): 803
## Town or small city :3049 East of England : 685
## Country :1749 South West (England): 646
## Refusal : 0 Scotland : 643
## Don't know : 0 London : 639
## No answer : 0 (Other) :2633
## health_disability left_right polintr_binary life_sat
## No :4822 Left (0-3) :1540 Yes:4117 High :5013
## Yes:2174 Moderate (4-6):4161 No :2879 Moderate:1493
## Right (7-10) :1295 Low : 490
##
##
##
##
## econ_sat immig_support daily_netuse country_attach post_polonline
## High :1229 High :2980 No :2131 High :4541 No :5385
## Moderate:3281 Moderate:2786 Yes:4865 Moderate:1634 Yes:1611
## Low :2486 Low :1230 Low : 821
##
##
##
## Regularisation - using lasso on imputed dataset
We still have 529 missing values in our target variables for difference and ratio over the 4 rounds. But this is fine. We can continue modelling.
# remove all those NA rows for our target. We can't model with them
final_data_regress <- final_data_regress |>
drop_na()Prepare data - dummify and set x+y matrices
We use the weights function within glmnet here rather than a survey object to support our anweight. This is actually fine and gives the same output because we haven’t considered the stratification or psu.
Change all columns to dummies to run the lasso. This is required to run the lasso cross validation and run.
# select cols to dummify
categorical_cols_to_dummy <- c(
"essround", "gender", "age_rec", "educ_level", "income_group", "income_stress", "area_type", "health_disability", "left_right",
"polintr_binary", "life_sat", "econ_sat", "immig_support", "daily_netuse", "country_attach", "post_polonline"
)
# check which variables we aren't dummifying.
setdiff(names(final_data_regress), categorical_cols_to_dummy)## [1] "anweight" "trust_europ" "target_diff" "target_ratiolog"
## [5] "region"# now create matrix with all our dummies.
data_with_dummies <- dummy_cols(
.data = final_data_regress,
select_columns = categorical_cols_to_dummy,
remove_first_dummy = TRUE, # Removes one dummy for each factor (e.g., gender_Female if Male is base)
remove_selected_columns = TRUE # Removes the original 'gender', 'essround' columns etc.
)- Setup the x and y matrix to call for cross validation lasso modelling.
# Prepare the response variable (y), the variable matrix and weights
y_lasso <- data_with_dummies |> select(target_diff) |> as.matrix()
x_lasso <- data_with_dummies |> select(-target_diff, -target_ratiolog, -trust_europ, -anweight, -region) |> as.matrix()
anweight_vector <- data_with_dummies$anweightRun the CV lasso on raw target variable
- Run the cross-validation lasso on the raw difference target variable.
# for reproducibility
set.seed(123)
cv_lasso_model <- cv.glmnet(
x = x_lasso,
y = y_lasso,
weights = anweight_vector,
alpha = 1,
family = "gaussian" # Still treat continuous
)
# Plot cross-validation results
plot(cv_lasso_model)
Check the outputs with the minimum lamda.
## 40 x 1 sparse Matrix of class "dgCMatrix"
## s1
## (Intercept) -0.294608941
## essround_9 0.061676101
## essround_10 0.401416656
## essround_11 0.573126570
## gender_Female 0.284749434
## gender_No answer .
## age_rec_20-29 -0.152414288
## age_rec_30-44 -0.182194706
## age_rec_45-64 -0.545995458
## age_rec_>=65 -0.822660551
## educ_level_Upper secondary -0.061208785
## educ_level_Tertiary -0.061462487
## income_group_Low (Decile <4) 0.116173444
## income_group_Moderate (Decile 4-7) -0.005900157
## income_stress_Coping on present income 0.093615164
## income_stress_Difficult on present income 0.067292383
## income_stress_Very difficult on present income -0.138546897
## income_stress_Refusal .
## income_stress_Don't know .
## income_stress_No answer .
## area_type_Suburbs or outskirts of big city -0.141102933
## area_type_Town or small city .
## area_type_Country -0.107548261
## area_type_Refusal .
## area_type_Don't know .
## area_type_No answer .
## health_disability_Yes -0.093479878
## left_right_Moderate (4-6) -0.649559518
## left_right_Right (7-10) -1.467935271
## polintr_binary_No 0.115513416
## life_sat_Moderate 0.003675170
## life_sat_Low 0.084828494
## econ_sat_Moderate 0.456800005
## econ_sat_Low 1.153711657
## immig_support_Moderate -0.709203498
## immig_support_Low -0.991714304
## daily_netuse_Yes 0.001437051
## country_attach_Moderate 0.389787220
## country_attach_Low 1.102898093
## post_polonline_Yes 0.306243829# You can then fit the final model using glmnet with the chosen lambda
model_lasso_lamdamin <- glmnet(
x = x_lasso,
y = y_lasso,
weights = anweight_vector,
alpha = 1,
family = "gaussian",
lambda = cv_lasso_model$lambda.min # Or lambda.1se
)Check the output with the lamda that provides the simplest lamda for simplest model within 1 standard deviation of the minimum value.
## 40 x 1 sparse Matrix of class "dgCMatrix"
## s1
## (Intercept) -0.2227912
## essround_9 .
## essround_10 0.1168645
## essround_11 0.3246018
## gender_Female 0.1260588
## gender_No answer .
## age_rec_20-29 .
## age_rec_30-44 .
## age_rec_45-64 -0.1733040
## age_rec_>=65 -0.4568298
## educ_level_Upper secondary .
## educ_level_Tertiary .
## income_group_Low (Decile <4) .
## income_group_Moderate (Decile 4-7) .
## income_stress_Coping on present income .
## income_stress_Difficult on present income .
## income_stress_Very difficult on present income .
## income_stress_Refusal .
## income_stress_Don't know .
## income_stress_No answer .
## area_type_Suburbs or outskirts of big city .
## area_type_Town or small city .
## area_type_Country .
## area_type_Refusal .
## area_type_Don't know .
## area_type_No answer .
## health_disability_Yes .
## left_right_Moderate (4-6) -0.3754365
## left_right_Right (7-10) -1.2614847
## polintr_binary_No .
## life_sat_Moderate .
## life_sat_Low .
## econ_sat_Moderate .
## econ_sat_Low 0.7304620
## immig_support_Moderate -0.4989241
## immig_support_Low -0.6716489
## daily_netuse_Yes .
## country_attach_Moderate 0.2920071
## country_attach_Low 0.9486191
## post_polonline_Yes 0.1986210# You can then fit the final model using glmnet with the chosen lambda
model_lasso_lamda1se <- glmnet(
x = x_lasso,
y = y_lasso,
weights = anweight_vector,
alpha = 1,
family = "gaussian",
lambda = cv_lasso_model$lambda.1se # Or lambda.1se
)Now extract the coefficients for both.
# Extract coefficients for min
coefs_lamdamin <- as.matrix(coef(model_lasso_lamdamin)) |>
as.data.frame() |>
rownames_to_column(var="Variable") |> rename(lamda_min = s0)
# extract for 1se
coefs_lamda1se <- as.matrix(coef(model_lasso_lamda1se)) |>
as.data.frame() |>
rownames_to_column(var="Variable") |> rename(lamda_1se = s0)
lasso_summary_raw <- coefs_lamdamin |> left_join(coefs_lamda1se) |>
mutate(lamda_min = ifelse(lamda_min == 0, NA, lamda_min),
lamda_1se = ifelse(lamda_1se == 0, NA, lamda_1se))## Joining with `by = join_by(Variable)`From this model we can see that our significant variables to include are:
Ess Round Gender Age Left/Right political Satisfaction with economy Support for immigrants Attachment to country Post political content online
Education (in larger model only) Income stress (in larger model only) Area type (in larger model only) Health/disability (in larger model only) Political Interest (larger only) life satisfaction (larger only) Daily internet user (larger only)
We see that the lamda minimum includes ALL variables. Thus we run the lamda at 1se as our lasso model.
trust_diff = essround + gender + age_rec + left_right + econ_sat + immig_support + country_attach + post_polonline
Run the CV lasso on log ratio target variable
#update target
y_lasso <- data_with_dummies |> select(target_ratiolog) |> as.matrix()
# for reproducibility
set.seed(123)
cv_lasso_model <- cv.glmnet(
x = x_lasso,
y = y_lasso,
weights = anweight_vector,
alpha = 1,
family = "gaussian" # Still treat continuous
)
# Get the coefficients at the optimal lambda (lambda.min) and 1se
coef(cv_lasso_model, s = "lambda.min")## 40 x 1 sparse Matrix of class "dgCMatrix"
## s1
## (Intercept) -0.199720148
## essround_9 .
## essround_10 0.124712595
## essround_11 0.206601344
## gender_Female 0.189992765
## gender_No answer .
## age_rec_20-29 .
## age_rec_30-44 -0.031540367
## age_rec_45-64 -0.175219607
## age_rec_>=65 -0.321111716
## educ_level_Upper secondary -0.007156668
## educ_level_Tertiary .
## income_group_Low (Decile <4) 0.043801032
## income_group_Moderate (Decile 4-7) 0.007235675
## income_stress_Coping on present income 0.026990147
## income_stress_Difficult on present income 0.009872455
## income_stress_Very difficult on present income -0.149406623
## income_stress_Refusal .
## income_stress_Don't know .
## income_stress_No answer .
## area_type_Suburbs or outskirts of big city -0.002864934
## area_type_Town or small city 0.027673620
## area_type_Country -0.046227372
## area_type_Refusal .
## area_type_Don't know .
## area_type_No answer .
## health_disability_Yes -0.069434947
## left_right_Moderate (4-6) -0.201071111
## left_right_Right (7-10) -0.555537372
## polintr_binary_No 0.076337077
## life_sat_Moderate -0.015283130
## life_sat_Low 0.124934565
## econ_sat_Moderate 0.031061377
## econ_sat_Low 0.324506353
## immig_support_Moderate -0.353476567
## immig_support_Low -0.689623799
## daily_netuse_Yes 0.077732898
## country_attach_Moderate 0.150665615
## country_attach_Low 0.488496920
## post_polonline_Yes 0.110472686## 40 x 1 sparse Matrix of class "dgCMatrix"
## s1
## (Intercept) -0.226931802
## essround_9 .
## essround_10 .
## essround_11 0.054453312
## gender_Female 0.065588267
## gender_No answer .
## age_rec_20-29 0.007929058
## age_rec_30-44 .
## age_rec_45-64 -0.014911998
## age_rec_>=65 -0.184263956
## educ_level_Upper secondary .
## educ_level_Tertiary .
## income_group_Low (Decile <4) .
## income_group_Moderate (Decile 4-7) .
## income_stress_Coping on present income .
## income_stress_Difficult on present income .
## income_stress_Very difficult on present income .
## income_stress_Refusal .
## income_stress_Don't know .
## income_stress_No answer .
## area_type_Suburbs or outskirts of big city .
## area_type_Town or small city .
## area_type_Country .
## area_type_Refusal .
## area_type_Don't know .
## area_type_No answer .
## health_disability_Yes .
## left_right_Moderate (4-6) -0.014714201
## left_right_Right (7-10) -0.371965960
## polintr_binary_No .
## life_sat_Moderate .
## life_sat_Low .
## econ_sat_Moderate .
## econ_sat_Low 0.255117187
## immig_support_Moderate -0.228975133
## immig_support_Low -0.498344913
## daily_netuse_Yes 0.056659475
## country_attach_Moderate 0.058919284
## country_attach_Low 0.353499456
## post_polonline_Yes 0.043489771# You can then fit the models and compare significant variables
model_lasso_lamdamin <- glmnet(
x = x_lasso,
y = y_lasso,
weights = anweight_vector,
alpha = 1,
family = "gaussian",
lambda = cv_lasso_model$lambda.min # Or lambda.1se
)
# Try with the 1se value too
model_lasso_lamda1se <- glmnet(
x = x_lasso,
y = y_lasso,
weights = anweight_vector,
alpha = 1,
family = "gaussian",
lambda = cv_lasso_model$lambda.1se # Or lambda.1se
)
# Extract coefficients for min
coefs_lamdamin <- as.matrix(coef(model_lasso_lamdamin)) |>
as.data.frame() |>
rownames_to_column(var="Variable") |> rename(lamda_min = s0)
# extract for 1se
coefs_lamda1se <- as.matrix(coef(model_lasso_lamda1se)) |>
as.data.frame() |>
rownames_to_column(var="Variable") |> rename(lamda_1se = s0)
lasso_summary_ratio <- coefs_lamdamin |> left_join(coefs_lamda1se) |>
mutate(lamda_min = ifelse(lamda_min == 0, NA, lamda_min),
lamda_1se = ifelse(lamda_1se == 0, NA, lamda_1se))## Joining with `by = join_by(Variable)`In this model we see the lamda min value still includes all values.
The lamda 1se model is similar: With daily netuse included as the only change. essround, gender, age, left_right, econ_sat, immig_support, daily_netuse, country_attach, post_polonline
Modelling the raw target difference variable
Run each model: null, base(time), full model, regularised model
## Null model
model_null_raw <- glm(target_diff ~ 1,
data = final_data_regress,
weights = anweight,
family = "gaussian")
## Base model of time only
model_time_raw <- glm(target_diff ~ essround,
data = final_data_regress,
weights = anweight,
family = "gaussian")
## full model
model_full_raw <- glm(target_diff ~ . -anweight - trust_europ - target_diff - target_ratiolog - region,
data = final_data_regress,
weights = anweight,
family = "gaussian")
## Regularised model - note this excludes netuse per above regularisation
model_reg_raw <- glm(target_diff ~ essround + gender + age_rec + left_right + econ_sat +
immig_support +country_attach + post_polonline,
data = final_data_regress,
weights = anweight,
family = "gaussian")
summary(model_full_raw)##
## Call:
## glm(formula = target_diff ~ . - anweight - trust_europ - target_diff -
## target_ratiolog - region, family = "gaussian", data = final_data_regress,
## weights = anweight)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -0.169574 0.193070 -0.878
## essround9 0.089658 0.078633 1.140
## essround10 0.425743 0.080634 5.280
## essround11 0.593878 0.084808 7.003
## genderFemale 0.290431 0.056600 5.131
## age_rec20-29 -0.297684 0.142872 -2.084
## age_rec30-44 -0.322264 0.136270 -2.365
## age_rec45-64 -0.680986 0.136315 -4.996
## age_rec>=65 -0.954295 0.144938 -6.584
## educ_levelUpper secondary -0.073470 0.081454 -0.902
## educ_levelTertiary -0.065907 0.076824 -0.858
## income_groupLow (Decile <4) 0.115759 0.086624 1.336
## income_groupModerate (Decile 4-7) -0.013119 0.070638 -0.186
## income_stressCoping on present income 0.106347 0.064585 1.647
## income_stressDifficult on present income 0.079305 0.105239 0.754
## income_stressVery difficult on present income -0.165674 0.187901 -0.882
## area_typeSuburbs or outskirts of big city -0.177110 0.098196 -1.804
## area_typeTown or small city -0.029689 0.089077 -0.333
## area_typeCountry -0.141425 0.098677 -1.433
## health_disabilityYes -0.104276 0.066656 -1.564
## left_rightModerate (4-6) -0.660554 0.072882 -9.063
## left_rightRight (7-10) -1.474950 0.097492 -15.129
## polintr_binaryNo 0.120271 0.063833 1.884
## life_satModerate 0.008211 0.073350 0.112
## life_satLow 0.109845 0.129646 0.847
## econ_satModerate 0.483565 0.081203 5.955
## econ_satLow 1.182593 0.093202 12.689
## immig_supportModerate -0.715877 0.065031 -11.008
## immig_supportLow -1.002881 0.092698 -10.819
## daily_netuseYes 0.002706 0.076455 0.035
## country_attachModerate 0.386667 0.069121 5.594
## country_attachLow 1.102276 0.091105 12.099
## post_polonlineYes 0.309458 0.068482 4.519
## Pr(>|t|)
## (Intercept) 0.3798
## essround9 0.2542
## essround10 1.33e-07 ***
## essround11 2.77e-12 ***
## genderFemale 2.96e-07 ***
## age_rec20-29 0.0372 *
## age_rec30-44 0.0181 *
## age_rec45-64 6.02e-07 ***
## age_rec>=65 4.94e-11 ***
## educ_levelUpper secondary 0.3671
## educ_levelTertiary 0.3910
## income_groupLow (Decile <4) 0.1815
## income_groupModerate (Decile 4-7) 0.8527
## income_stressCoping on present income 0.0997 .
## income_stressDifficult on present income 0.4511
## income_stressVery difficult on present income 0.3780
## area_typeSuburbs or outskirts of big city 0.0713 .
## area_typeTown or small city 0.7389
## area_typeCountry 0.1518
## health_disabilityYes 0.1178
## left_rightModerate (4-6) < 2e-16 ***
## left_rightRight (7-10) < 2e-16 ***
## polintr_binaryNo 0.0596 .
## life_satModerate 0.9109
## life_satLow 0.3969
## econ_satModerate 2.74e-09 ***
## econ_satLow < 2e-16 ***
## immig_supportModerate < 2e-16 ***
## immig_supportLow < 2e-16 ***
## daily_netuseYes 0.9718
## country_attachModerate 2.31e-08 ***
## country_attachLow < 2e-16 ***
## post_polonlineYes 6.33e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 15.61016)
##
## Null deviance: 131347 on 6466 degrees of freedom
## Residual deviance: 100436 on 6434 degrees of freedom
## AIC: 29917
##
## Number of Fisher Scoring iterations: 2Model evaluation
Now we compare the outputs: a) AIC and BIC b) anova measures
## First we compare the AIC and BIC:
AIC(model_null_raw, model_time_raw, model_full_raw, model_reg_raw)We see the regularised model performs best. The one without netuse included:
Now we compare each level of nested model:
The anova indicates that the more complex model (the second one listed) provides a statistically significant improvement in fit over the simpler model (the first one). However the AIC indicates the simpler regularised model is best.
Now display to compare models with modelsummary:
modelsummary(
list("Null" = model_null_raw,
"Time Only" = model_time_raw,
"Regularised" = model_reg_raw,
"Full" = model_full_raw),
metrics = c("AIC", "BIC", "RMSE", "R2"), # R2 might be pseudo R2 for GLM
gof_map = c("nobs", "aic", "bic"), # You can customize which GOF statistics to show
stars = TRUE, # Add significance stars
title = "Comparison of GLM Models with Weights"
)| Null | Time Only | Regularised | Full | |
|---|---|---|---|---|
| + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001 | ||||
| (Intercept) | -0.432*** | -0.932*** | -0.158 | -0.170 |
| (0.032) | (0.062) | (0.159) | (0.193) | |
| essround9 | 0.174* | 0.091 | 0.090 | |
| (0.087) | (0.078) | (0.079) | ||
| essround10 | 0.751*** | 0.402*** | 0.426*** | |
| (0.087) | (0.080) | (0.081) | ||
| essround11 | 1.113*** | 0.600*** | 0.594*** | |
| (0.089) | (0.084) | (0.085) | ||
| genderFemale | 0.311*** | 0.290*** | ||
| (0.056) | (0.057) | |||
| age_rec20-29 | -0.327* | -0.298* | ||
| (0.138) | (0.143) | |||
| age_rec30-44 | -0.377** | -0.322* | ||
| (0.130) | (0.136) | |||
| age_rec45-64 | -0.772*** | -0.681*** | ||
| (0.129) | (0.136) | |||
| age_rec>=65 | -1.036*** | -0.954*** | ||
| (0.135) | (0.145) | |||
| left_rightModerate (4-6) | -0.650*** | -0.661*** | ||
| (0.072) | (0.073) | |||
| left_rightRight (7-10) | -1.493*** | -1.475*** | ||
| (0.097) | (0.097) | |||
| econ_satModerate | 0.476*** | 0.484*** | ||
| (0.080) | (0.081) | |||
| econ_satLow | 1.187*** | 1.183*** | ||
| (0.091) | (0.093) | |||
| immig_supportModerate | -0.682*** | -0.716*** | ||
| (0.064) | (0.065) | |||
| immig_supportLow | -0.934*** | -1.003*** | ||
| (0.088) | (0.093) | |||
| country_attachModerate | 0.410*** | 0.387*** | ||
| (0.069) | (0.069) | |||
| country_attachLow | 1.131*** | 1.102*** | ||
| (0.090) | (0.091) | |||
| post_polonlineYes | 0.257*** | 0.309*** | ||
| (0.066) | (0.068) | |||
| educ_levelUpper secondary | -0.073 | |||
| (0.081) | ||||
| educ_levelTertiary | -0.066 | |||
| (0.077) | ||||
| income_groupLow (Decile <4) | 0.116 | |||
| (0.087) | ||||
| income_groupModerate (Decile 4-7) | -0.013 | |||
| (0.071) | ||||
| income_stressCoping on present income | 0.106+ | |||
| (0.065) | ||||
| income_stressDifficult on present income | 0.079 | |||
| (0.105) | ||||
| income_stressVery difficult on present income | -0.166 | |||
| (0.188) | ||||
| area_typeSuburbs or outskirts of big city | -0.177+ | |||
| (0.098) | ||||
| area_typeTown or small city | -0.030 | |||
| (0.089) | ||||
| area_typeCountry | -0.141 | |||
| (0.099) | ||||
| health_disabilityYes | -0.104 | |||
| (0.067) | ||||
| polintr_binaryNo | 0.120+ | |||
| (0.064) | ||||
| life_satModerate | 0.008 | |||
| (0.073) | ||||
| life_satLow | 0.110 | |||
| (0.130) | ||||
| daily_netuseYes | 0.003 | |||
| (0.076) | ||||
| Num.Obs. | 6467 | 6467 | 6467 | 6467 |
| AIC | 54150.5 | 53937.3 | 52067.1 | 52027.8 |
| BIC | 54164.0 | 53971.1 | 52195.9 | 52258.1 |
We select the regularised model as the best. It is observed that the following variables are significant:
##
## Call:
## glm(formula = target_diff ~ essround + gender + age_rec + left_right +
## econ_sat + immig_support + country_attach + post_polonline,
## family = "gaussian", data = final_data_regress, weights = anweight)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.15789 0.15852 -0.996 0.31930
## essround9 0.09068 0.07831 1.158 0.24689
## essround10 0.40167 0.07987 5.029 5.07e-07 ***
## essround11 0.59973 0.08367 7.168 8.46e-13 ***
## genderFemale 0.31145 0.05589 5.573 2.61e-08 ***
## age_rec20-29 -0.32720 0.13751 -2.379 0.01737 *
## age_rec30-44 -0.37731 0.13001 -2.902 0.00372 **
## age_rec45-64 -0.77189 0.12947 -5.962 2.63e-09 ***
## age_rec>=65 -1.03559 0.13529 -7.655 2.22e-14 ***
## left_rightModerate (4-6) -0.64982 0.07249 -8.964 < 2e-16 ***
## left_rightRight (7-10) -1.49264 0.09690 -15.405 < 2e-16 ***
## econ_satModerate 0.47563 0.08049 5.909 3.61e-09 ***
## econ_satLow 1.18713 0.09060 13.103 < 2e-16 ***
## immig_supportModerate -0.68209 0.06361 -10.724 < 2e-16 ***
## immig_supportLow -0.93438 0.08845 -10.564 < 2e-16 ***
## country_attachModerate 0.40975 0.06868 5.966 2.57e-09 ***
## country_attachLow 1.13148 0.09001 12.571 < 2e-16 ***
## post_polonlineYes 0.25659 0.06581 3.899 9.76e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 15.63814)
##
## Null deviance: 131347 on 6466 degrees of freedom
## Residual deviance: 100850 on 6449 degrees of freedom
## AIC: 29914
##
## Number of Fisher Scoring iterations: 2The following are statistically significantly associated with increased relative support for EU parliament to the UK parliament:
Time, ESS round 10 and 11 all had signficantly higher support than Round 8.
Being female.
Being <20 years old (compared to all other age groups. 1+ point relative support more than 65+ )
Being left leaning politically (including +1.5 points more relative EU support than right leaning people)
Being dissatisfied with the economy (+1.2 points relative support more than people satisfied with the economy)
Supporting immigration (+0.93 points compared to those with low immigrant sentiment)
Having low attachment to the UK (+1.1 points compared to high attachment)
Posting political content online (+0.3 points to those who don't)Modelling the ratio target variable.
Run each model: null, base(time), full model, regularised model
## Null model
model_null_ratio <- glm(target_ratiolog ~ 1,
data = final_data_regress,
weights = anweight,
family = "gaussian")
## Base model of time only
model_time_ratio <- glm(target_ratiolog ~ essround,
data = final_data_regress,
weights = anweight,
family = "gaussian")
## full model
model_full_ratio <- glm(target_ratiolog ~ . -anweight - trust_europ - target_diff - target_ratiolog - region,
data = final_data_regress,
weights = anweight,
family = "gaussian")
## Regularised model -includes netuse due to regularisation method.
model_reg_ratio <- glm(target_ratiolog ~ essround + gender + age_rec + left_right + econ_sat +
immig_support + daily_netuse +country_attach + post_polonline,
data = final_data_regress,
weights = anweight,
family = "gaussian")Model evaluation (ratios)
Now we compare the outputs: a) AIC and BIC b) anova measures
## First we compare the AIC and BIC:
AIC(model_null_ratio, model_time_ratio, model_full_ratio, model_reg_ratio)We see that the regularised model is the best fit with both AIC and BIC.
And the nested model comparisons with Anova
# Compare Null vs. Time
anova(model_null_ratio, model_time_ratio, test = "Chisq") # "Chisq" for GLMsAgain, the anova estimates the full model is an improvemnet on the regularised model. The residual deviation is comparable so we prefer the simplified model for interpretation and simplicity.
Now display to compare models with modelsummary:
modelsummary(
list("Null" = model_null_ratio,
"Time Only" = model_time_ratio,
"Regularised" = model_reg_ratio,
"Full" = model_full_ratio),
metrics = c("AIC", "BIC", "RMSE", "R2"), # R2 might be pseudo R2 for GLM
gof_map = c("nobs", "aic", "bic"), # You can customize which GOF statistics to show
stars = TRUE, # Add significance stars
title = "Comparison of GLM Models with Weights"
)| Null | Time Only | Regularised | Full | |
|---|---|---|---|---|
| + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001 | ||||
| (Intercept) | -0.253*** | -0.440*** | -0.069 | -0.141 |
| (0.017) | (0.034) | (0.098) | (0.112) | |
| essround9 | 0.046 | 0.012 | 0.005 | |
| (0.049) | (0.045) | (0.046) | ||
| essround10 | 0.286*** | 0.132** | 0.140** | |
| (0.048) | (0.046) | (0.047) | ||
| essround11 | 0.432*** | 0.222*** | 0.219*** | |
| (0.050) | (0.049) | (0.049) | ||
| genderFemale | 0.207*** | 0.195*** | ||
| (0.032) | (0.033) | |||
| age_rec20-29 | -0.089 | -0.096 | ||
| (0.080) | (0.083) | |||
| age_rec30-44 | -0.142+ | -0.137+ | ||
| (0.075) | (0.079) | |||
| age_rec45-64 | -0.304*** | -0.275*** | ||
| (0.075) | (0.079) | |||
| age_rec>=65 | -0.459*** | -0.424*** | ||
| (0.080) | (0.084) | |||
| left_rightModerate (4-6) | -0.208*** | -0.214*** | ||
| (0.042) | (0.042) | |||
| left_rightRight (7-10) | -0.575*** | -0.564*** | ||
| (0.056) | (0.056) | |||
| econ_satModerate | 0.055 | 0.062 | ||
| (0.047) | (0.047) | |||
| econ_satLow | 0.355*** | 0.358*** | ||
| (0.052) | (0.054) | |||
| immig_supportModerate | -0.349*** | -0.363*** | ||
| (0.037) | (0.038) | |||
| immig_supportLow | -0.677*** | -0.704*** | ||
| (0.052) | (0.054) | |||
| daily_netuseYes | 0.059 | 0.079+ | ||
| (0.043) | (0.044) | |||
| country_attachModerate | 0.161*** | 0.149*** | ||
| (0.040) | (0.040) | |||
| country_attachLow | 0.508*** | 0.490*** | ||
| (0.052) | (0.053) | |||
| post_polonlineYes | 0.088* | 0.115** | ||
| (0.038) | (0.040) | |||
| educ_levelUpper secondary | -0.015 | |||
| (0.047) | ||||
| educ_levelTertiary | 0.007 | |||
| (0.044) | ||||
| income_groupLow (Decile <4) | 0.069 | |||
| (0.050) | ||||
| income_groupModerate (Decile 4-7) | 0.028 | |||
| (0.041) | ||||
| income_stressCoping on present income | 0.036 | |||
| (0.037) | ||||
| income_stressDifficult on present income | 0.021 | |||
| (0.061) | ||||
| income_stressVery difficult on present income | -0.184+ | |||
| (0.109) | ||||
| area_typeSuburbs or outskirts of big city | -0.016 | |||
| (0.057) | ||||
| area_typeTown or small city | 0.028 | |||
| (0.052) | ||||
| area_typeCountry | -0.055 | |||
| (0.057) | ||||
| health_disabilityYes | -0.081* | |||
| (0.039) | ||||
| polintr_binaryNo | 0.084* | |||
| (0.037) | ||||
| life_satModerate | -0.035 | |||
| (0.042) | ||||
| life_satLow | 0.144+ | |||
| (0.075) | ||||
| Num.Obs. | 6467 | 6467 | 6467 | 6467 |
| AIC | 44087.2 | 43979.2 | 42717.7 | 42645.7 |
| BIC | 44100.7 | 44013.1 | 42853.2 | 42876.1 |
We also see that daily net use isn’t significant in this ratio model.
Otherwise the trends are identical.
Interpreting the ratio model outputs:
Interpreting the ratios outputs: We need to take the exponential of the estimate, given our estimate is log(trustEU / trustUK).
Where for example, being female [estimate = 0.20 with exp(estimate) = 1.22]. This can also be stated as: Being female is associated with a 22.51% higher ratio of trust in the EU Parliament to UK parliament compared to being male, holding other factors constant.
# use broom to add exponential and print interpretation column.
broom::tidy(model_reg_ratio) |>
mutate(exp_estimate = exp(estimate),
interpretation = ifelse(exp_estimate<1,
glue("{round((exp_estimate-1)*100,2)}%"),
glue("+{round((exp_estimate-1)*100,2)}%")),
sig5pct = ifelse(p.value < 0.05, "YES", "no"),
.after = estimate) |>
filter(sig5pct == "YES")Models with interaction terms:
Modelling with time (essround) as a factor
With this interaction, we compare the interaction terms to a base period which is unreported e.g. for essround10:left_rightModerate (4-6) AND essround11:left_rightRight (7-10), the base group will be the unreported value: which is essround8:left_rightLeft (0-3)
Select variables that we want to target that are are most associated with the “remain” voters to see if their opinions have intensified: - country_attachment -> for britishness - Immigration_support -> for anti-immigrant sentiment - Left-right -> for political positioning
## Regularised model WITH interactions
model_regint_raw <- glm(target_diff ~ essround + gender + age_rec + left_right*essround + econ_sat +
immig_support*essround + country_attach*essround + post_polonline,
data = final_data_regress,
weights = anweight,
family = "gaussian")
summary(model_regint_raw)##
## Call:
## glm(formula = target_diff ~ essround + gender + age_rec + left_right *
## essround + econ_sat + immig_support * essround + country_attach *
## essround + post_polonline, family = "gaussian", data = final_data_regress,
## weights = anweight)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.34396 0.19777 -1.739 0.082051 .
## essround9 0.01513 0.19829 0.076 0.939197
## essround10 0.78293 0.19228 4.072 4.72e-05 ***
## essround11 0.89847 0.19862 4.524 6.19e-06 ***
## genderFemale 0.30097 0.05586 5.388 7.38e-08 ***
## age_rec20-29 -0.32384 0.13782 -2.350 0.018820 *
## age_rec30-44 -0.35468 0.13014 -2.725 0.006441 **
## age_rec45-64 -0.75239 0.12961 -5.805 6.75e-09 ***
## age_rec>=65 -1.01512 0.13550 -7.492 7.70e-14 ***
## left_rightModerate (4-6) -0.59266 0.14984 -3.955 7.73e-05 ***
## left_rightRight (7-10) -1.59212 0.19476 -8.175 3.54e-16 ***
## econ_satModerate 0.45895 0.08067 5.690 1.33e-08 ***
## econ_satLow 1.18402 0.09059 13.070 < 2e-16 ***
## immig_supportModerate -0.38360 0.12457 -3.079 0.002082 **
## immig_supportLow -0.73851 0.16361 -4.514 6.48e-06 ***
## country_attachModerate 0.56806 0.13300 4.271 1.97e-05 ***
## country_attachLow 0.76772 0.18592 4.129 3.68e-05 ***
## post_polonlineYes 0.25155 0.06581 3.823 0.000133 ***
## essround9:left_rightModerate (4-6) 0.15327 0.20644 0.742 0.457851
## essround10:left_rightModerate (4-6) -0.20605 0.20225 -1.019 0.308345
## essround11:left_rightModerate (4-6) -0.10734 0.20379 -0.527 0.598408
## essround9:left_rightRight (7-10) 0.42281 0.26506 1.595 0.110730
## essround10:left_rightRight (7-10) -0.20435 0.26284 -0.777 0.436904
## essround11:left_rightRight (7-10) 0.31574 0.27096 1.165 0.243947
## essround9:immig_supportModerate -0.13226 0.17614 -0.751 0.452739
## essround10:immig_supportModerate -0.60949 0.17701 -3.443 0.000578 ***
## essround11:immig_supportModerate -0.43736 0.17886 -2.445 0.014502 *
## essround9:immig_supportLow -0.07909 0.22641 -0.349 0.726852
## essround10:immig_supportLow -0.17180 0.25281 -0.680 0.496793
## essround11:immig_supportLow -0.49221 0.24469 -2.012 0.044310 *
## essround9:country_attachModerate -0.12189 0.18697 -0.652 0.514461
## essround10:country_attachModerate -0.13948 0.18611 -0.749 0.453620
## essround11:country_attachModerate -0.37350 0.18930 -1.973 0.048532 *
## essround9:country_attachLow 0.20917 0.25098 0.833 0.404646
## essround10:country_attachLow 0.66035 0.25084 2.633 0.008495 **
## essround11:country_attachLow 0.51541 0.25319 2.036 0.041824 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 15.56227)
##
## Null deviance: 131347 on 6466 degrees of freedom
## Residual deviance: 100081 on 6431 degrees of freedom
## AIC: 29900
##
## Number of Fisher Scoring iterations: 2broom::tidy(model_regint_raw) |>
mutate(sig5pct = ifelse(p.value < 0.05, "YES", "no"),
.after = estimate)model_regint_raw2 <- glm(target_diff ~ essround + essround + age_rec*essround + left_right*essround + econ_sat +
immig_support*essround + country_attach*essround + post_polonline,
data = final_data_regress,
weights = anweight,
family = "gaussian")
summary(model_regint_raw2)##
## Call:
## glm(formula = target_diff ~ essround + essround + age_rec * essround +
## left_right * essround + econ_sat + immig_support * essround +
## country_attach * essround + post_polonline, family = "gaussian",
## data = final_data_regress, weights = anweight)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.18986 0.26840 0.707 0.479364
## essround9 -0.19850 0.37837 -0.525 0.599869
## essround10 -0.13188 0.36730 -0.359 0.719579
## essround11 0.36865 0.39160 0.941 0.346548
## age_rec20-29 -0.69043 0.26004 -2.655 0.007948 **
## age_rec30-44 -0.88927 0.24661 -3.606 0.000313 ***
## age_rec45-64 -1.05088 0.24357 -4.315 1.62e-05 ***
## age_rec>=65 -1.52970 0.25657 -5.962 2.62e-09 ***
## left_rightModerate (4-6) -0.62775 0.15005 -4.184 2.91e-05 ***
## left_rightRight (7-10) -1.62973 0.19548 -8.337 < 2e-16 ***
## econ_satModerate 0.50898 0.08065 6.311 2.97e-10 ***
## econ_satLow 1.21294 0.09058 13.391 < 2e-16 ***
## immig_supportModerate -0.35489 0.12528 -2.833 0.004630 **
## immig_supportLow -0.69082 0.16596 -4.163 3.19e-05 ***
## country_attachModerate 0.53802 0.13525 3.978 7.02e-05 ***
## country_attachLow 0.71502 0.18756 3.812 0.000139 ***
## post_polonlineYes 0.25291 0.06599 3.832 0.000128 ***
## essround9:age_rec20-29 0.25829 0.37888 0.682 0.495439
## essround10:age_rec20-29 1.10534 0.37674 2.934 0.003358 **
## essround11:age_rec20-29 0.07892 0.39296 0.201 0.840839
## essround9:age_rec30-44 0.24451 0.35660 0.686 0.492948
## essround10:age_rec30-44 1.26542 0.35664 3.548 0.000391 ***
## essround11:age_rec30-44 0.65232 0.37152 1.756 0.079166 .
## essround9:age_rec45-64 0.12337 0.35150 0.351 0.725626
## essround10:age_rec45-64 0.63822 0.35279 1.809 0.070487 .
## essround11:age_rec45-64 0.49376 0.36845 1.340 0.180255
## essround9:age_rec>=65 0.30058 0.37040 0.812 0.417102
## essround10:age_rec>=65 1.07465 0.36871 2.915 0.003573 **
## essround11:age_rec>=65 0.72415 0.38381 1.887 0.059242 .
## essround9:left_rightModerate (4-6) 0.19577 0.20677 0.947 0.343779
## essround10:left_rightModerate (4-6) -0.19584 0.20279 -0.966 0.334220
## essround11:left_rightModerate (4-6) -0.08627 0.20450 -0.422 0.673140
## essround9:left_rightRight (7-10) 0.44521 0.26659 1.670 0.094971 .
## essround10:left_rightRight (7-10) -0.21194 0.26538 -0.799 0.424544
## essround11:left_rightRight (7-10) 0.29597 0.27269 1.085 0.277796
## essround9:immig_supportModerate -0.16177 0.17751 -0.911 0.362166
## essround10:immig_supportModerate -0.62886 0.17816 -3.530 0.000419 ***
## essround11:immig_supportModerate -0.48217 0.18069 -2.669 0.007638 **
## essround9:immig_supportLow -0.11949 0.23047 -0.518 0.604172
## essround10:immig_supportLow -0.16136 0.25606 -0.630 0.528621
## essround11:immig_supportLow -0.58533 0.24930 -2.348 0.018909 *
## essround9:country_attachModerate -0.11879 0.19197 -0.619 0.536081
## essround10:country_attachModerate -0.13721 0.19196 -0.715 0.474790
## essround11:country_attachModerate -0.26344 0.19500 -1.351 0.176753
## essround9:country_attachLow 0.22013 0.25561 0.861 0.389157
## essround10:country_attachLow 0.68383 0.25529 2.679 0.007411 **
## essround11:country_attachLow 0.68336 0.25927 2.636 0.008416 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 15.58446)
##
## Null deviance: 131347 on 6466 degrees of freedom
## Residual deviance: 100052 on 6420 degrees of freedom
## AIC: 29920
##
## Number of Fisher Scoring iterations: 2broom::tidy(model_regint_raw2) |>
mutate(sig5pct = ifelse(p.value < 0.05, "YES", "no"),
.after = estimate)With raw values, we see that some of the interaction terms are signficant:
Immigration support: baseline group = High support in Round 8 essround10:immig_supportModerate (est = -0.6) essround11:immig_supportModerate (est = -0.44) essround11:immig_supportLow (est = -0.49)
Attachment to country: baseline = High attachment to UK in Round 8. essround11:country_attachModerate (est = -0.37) essround10:country_attachLow (est = +0.66) essround11:country_attachLow (est = +0.51)
# and for ratio interactions
model_regint_ratio <- glm(target_ratiolog ~ essround + gender*essround + age_rec*essround + left_right*essround + econ_sat*essround +
immig_support*essround + country_attach*essround + post_polonline*essround,
data = final_data_regress,
weights = anweight,
family = "gaussian")
summary(model_regint_ratio)##
## Call:
## glm(formula = target_ratiolog ~ essround + gender * essround +
## age_rec * essround + left_right * essround + econ_sat * essround +
## immig_support * essround + country_attach * essround + post_polonline *
## essround, family = "gaussian", data = final_data_regress,
## weights = anweight)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.520e-01 1.669e-01 0.910 0.362737
## essround9 4.559e-02 2.406e-01 0.189 0.849708
## essround10 -3.036e-01 2.436e-01 -1.246 0.212669
## essround11 -1.829e-01 2.645e-01 -0.691 0.489406
## genderFemale 1.667e-01 6.477e-02 2.574 0.010074 *
## age_rec20-29 -1.982e-01 1.509e-01 -1.314 0.188962
## age_rec30-44 -3.762e-01 1.429e-01 -2.633 0.008483 **
## age_rec45-64 -4.296e-01 1.417e-01 -3.032 0.002435 **
## age_rec>=65 -7.926e-01 1.499e-01 -5.287 1.28e-07 ***
## left_rightModerate (4-6) -2.195e-01 8.849e-02 -2.481 0.013141 *
## left_rightRight (7-10) -6.134e-01 1.168e-01 -5.252 1.55e-07 ***
## econ_satModerate 1.470e-01 7.930e-02 1.854 0.063742 .
## econ_satLow 3.631e-01 9.887e-02 3.672 0.000242 ***
## immig_supportModerate -2.567e-01 7.306e-02 -3.514 0.000445 ***
## immig_supportLow -7.167e-01 9.705e-02 -7.385 1.72e-13 ***
## country_attachModerate 2.129e-01 7.858e-02 2.709 0.006769 **
## country_attachLow 2.375e-01 1.102e-01 2.155 0.031173 *
## post_polonlineYes 6.701e-05 7.344e-02 0.001 0.999272
## essround9:genderFemale 6.403e-02 9.110e-02 0.703 0.482193
## essround10:genderFemale 4.077e-02 9.124e-02 0.447 0.654996
## essround11:genderFemale 3.628e-02 9.284e-02 0.391 0.696002
## essround9:age_rec20-29 6.939e-02 2.202e-01 0.315 0.752706
## essround10:age_rec20-29 3.909e-01 2.191e-01 1.784 0.074543 .
## essround11:age_rec20-29 1.220e-02 2.283e-01 0.053 0.957390
## essround9:age_rec30-44 2.234e-02 2.071e-01 0.108 0.914101
## essround10:age_rec30-44 6.182e-01 2.079e-01 2.974 0.002952 **
## essround11:age_rec30-44 3.872e-01 2.162e-01 1.791 0.073350 .
## essround9:age_rec45-64 -6.292e-02 2.051e-01 -0.307 0.759085
## essround10:age_rec45-64 2.625e-01 2.069e-01 1.269 0.204633
## essround11:age_rec45-64 3.752e-01 2.160e-01 1.737 0.082419 .
## essround9:age_rec>=65 5.683e-02 2.169e-01 0.262 0.793294
## essround10:age_rec>=65 6.847e-01 2.168e-01 3.159 0.001593 **
## essround11:age_rec>=65 5.526e-01 2.249e-01 2.457 0.014034 *
## essround9:left_rightModerate (4-6) 4.431e-02 1.221e-01 0.363 0.716783
## essround10:left_rightModerate (4-6) -2.076e-02 1.205e-01 -0.172 0.863214
## essround11:left_rightModerate (4-6) 1.364e-02 1.218e-01 0.112 0.910792
## essround9:left_rightRight (7-10) 8.748e-02 1.603e-01 0.546 0.585359
## essround10:left_rightRight (7-10) -3.555e-02 1.604e-01 -0.222 0.824603
## essround11:left_rightRight (7-10) 1.208e-01 1.641e-01 0.736 0.461541
## essround9:econ_satModerate -1.852e-01 1.152e-01 -1.608 0.107949
## essround10:econ_satModerate -1.038e-01 1.296e-01 -0.801 0.423124
## essround11:econ_satModerate -1.267e-02 1.551e-01 -0.082 0.934921
## essround9:econ_satLow -6.314e-02 1.403e-01 -0.450 0.652700
## essround10:econ_satLow -2.355e-02 1.467e-01 -0.161 0.872434
## essround11:econ_satLow 7.335e-02 1.676e-01 0.438 0.661584
## essround9:immig_supportModerate -4.548e-02 1.036e-01 -0.439 0.660784
## essround10:immig_supportModerate -1.844e-01 1.038e-01 -1.777 0.075577 .
## essround11:immig_supportModerate -1.781e-01 1.052e-01 -1.693 0.090560 .
## essround9:immig_supportLow 1.494e-01 1.358e-01 1.100 0.271581
## essround10:immig_supportLow 1.559e-01 1.498e-01 1.040 0.298160
## essround11:immig_supportLow -1.419e-01 1.466e-01 -0.968 0.333156
## essround9:country_attachModerate -1.380e-01 1.117e-01 -1.236 0.216672
## essround10:country_attachModerate -5.278e-02 1.117e-01 -0.472 0.636682
## essround11:country_attachModerate -1.493e-02 1.136e-01 -0.131 0.895454
## essround9:country_attachLow 1.277e-01 1.506e-01 0.848 0.396563
## essround10:country_attachLow 4.289e-01 1.503e-01 2.854 0.004327 **
## essround11:country_attachLow 5.072e-01 1.523e-01 3.331 0.000870 ***
## essround9:post_polonlineYes 6.505e-02 1.062e-01 0.613 0.540204
## essround10:post_polonlineYes 2.343e-01 1.048e-01 2.236 0.025367 *
## essround11:post_polonlineYes 8.109e-02 1.096e-01 0.740 0.459543
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 5.21535)
##
## Null deviance: 39849 on 6466 degrees of freedom
## Residual deviance: 33415 on 6407 degrees of freedom
## AIC: 22854
##
## Number of Fisher Scoring iterations: 2broom::tidy(model_regint_ratio) |>
mutate(exp_estimate = exp(estimate),
interpretation = ifelse(exp_estimate<1,
glue("{round((exp_estimate-1)*100,2)}%"),
glue("+{round((exp_estimate-1)*100,2)}%")),
sig5pct = ifelse(p.value < 0.05, "YES", "no"),
.after = estimate)# and for ratio interactions
model_regint_ratio2 <- glm(target_ratiolog ~ essround + gender + age_rec*essround + left_right + econ_sat +
immig_support*essround + daily_netuse + country_attach*essround + post_polonline,
data = final_data_regress,
weights = anweight,
family = "gaussian")
broom::tidy(model_regint_ratio2) |>
mutate(exp_estimate = exp(estimate),
interpretation = ifelse(exp_estimate<1,
glue("{round((exp_estimate-1)*100,2)}%"),
glue("+{round((exp_estimate-1)*100,2)}%")),
sig5pct = ifelse(p.value < 0.05, "YES", "no"),
.after = estimate)### MODEL PERFORMANCE EVALUATION - COMPARING INTERACTION TERMS.
AIC(model_regint_ratio, model_regint_ratio2)modelsummary(
list("Regularised" = model_reg_ratio,
"Interaction terms" = model_regint_ratio,
"Select interaction terms" = model_regint_ratio2),
metrics = c("AIC", "BIC", "RMSE", "R2"), # R2 might be pseudo R2 for GLM
gof_map = c("nobs", "aic", "bic"), # You can customize which GOF statistics to show
stars = TRUE, # Add significance stars
title = "Comparison of GLM Models with Weights"
)| Regularised | Interaction terms | Select interaction terms | |
|---|---|---|---|
| + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001 | |||
| (Intercept) | -0.069 | 0.152 | 0.067 |
| (0.098) | (0.167) | (0.150) | |
| essround9 | 0.012 | 0.046 | 0.057 |
| (0.045) | (0.241) | (0.199) | |
| essround10 | 0.132** | -0.304 | -0.238 |
| (0.046) | (0.244) | (0.198) | |
| essround11 | 0.222*** | -0.183 | -0.086 |
| (0.049) | (0.265) | (0.207) | |
| genderFemale | 0.207*** | 0.167* | 0.203*** |
| (0.032) | (0.065) | (0.032) | |
| age_rec20-29 | -0.089 | -0.198 | -0.201 |
| (0.080) | (0.151) | (0.150) | |
| age_rec30-44 | -0.142+ | -0.376** | -0.367** |
| (0.075) | (0.143) | (0.143) | |
| age_rec45-64 | -0.304*** | -0.430** | -0.409** |
| (0.075) | (0.142) | (0.141) | |
| age_rec>=65 | -0.459*** | -0.793*** | -0.741*** |
| (0.080) | (0.150) | (0.149) | |
| left_rightModerate (4-6) | -0.208*** | -0.219* | -0.210*** |
| (0.042) | (0.088) | (0.042) | |
| left_rightRight (7-10) | -0.575*** | -0.613*** | -0.573*** |
| (0.056) | (0.117) | (0.056) | |
| econ_satModerate | 0.055 | 0.147+ | 0.063 |
| (0.047) | (0.079) | (0.047) | |
| econ_satLow | 0.355*** | 0.363*** | 0.356*** |
| (0.052) | (0.099) | (0.052) | |
| immig_supportModerate | -0.349*** | -0.257*** | -0.239*** |
| (0.037) | (0.073) | (0.071) | |
| immig_supportLow | -0.677*** | -0.717*** | -0.707*** |
| (0.052) | (0.097) | (0.095) | |
| daily_netuseYes | 0.059 | 0.057 | |
| (0.043) | (0.043) | ||
| country_attachModerate | 0.161*** | 0.213** | 0.223** |
| (0.040) | (0.079) | (0.078) | |
| country_attachLow | 0.508*** | 0.237* | 0.234* |
| (0.052) | (0.110) | (0.108) | |
| post_polonlineYes | 0.088* | 0.000 | 0.089* |
| (0.038) | (0.073) | (0.039) | |
| essround9 × genderFemale | 0.064 | ||
| (0.091) | |||
| essround10 × genderFemale | 0.041 | ||
| (0.091) | |||
| essround11 × genderFemale | 0.036 | ||
| (0.093) | |||
| essround9 × age_rec20-29 | 0.069 | 0.057 | |
| (0.220) | (0.219) | ||
| essround10 × age_rec20-29 | 0.391+ | 0.369+ | |
| (0.219) | (0.218) | ||
| essround11 × age_rec20-29 | 0.012 | 0.022 | |
| (0.228) | (0.227) | ||
| essround9 × age_rec30-44 | 0.022 | 0.001 | |
| (0.207) | (0.206) | ||
| essround10 × age_rec30-44 | 0.618** | 0.591** | |
| (0.208) | (0.206) | ||
| essround11 × age_rec30-44 | 0.387+ | 0.388+ | |
| (0.216) | (0.214) | ||
| essround9 × age_rec45-64 | -0.063 | -0.089 | |
| (0.205) | (0.203) | ||
| essround10 × age_rec45-64 | 0.262 | 0.215 | |
| (0.207) | (0.204) | ||
| essround11 × age_rec45-64 | 0.375+ | 0.376+ | |
| (0.216) | (0.213) | ||
| essround9 × age_rec>=65 | 0.057 | 0.026 | |
| (0.217) | (0.214) | ||
| essround10 × age_rec>=65 | 0.685** | 0.602** | |
| (0.217) | (0.212) | ||
| essround11 × age_rec>=65 | 0.553* | 0.542* | |
| (0.225) | (0.221) | ||
| essround9 × left_rightModerate (4-6) | 0.044 | ||
| (0.122) | |||
| essround10 × left_rightModerate (4-6) | -0.021 | ||
| (0.120) | |||
| essround11 × left_rightModerate (4-6) | 0.014 | ||
| (0.122) | |||
| essround9 × left_rightRight (7-10) | 0.087 | ||
| (0.160) | |||
| essround10 × left_rightRight (7-10) | -0.036 | ||
| (0.160) | |||
| essround11 × left_rightRight (7-10) | 0.121 | ||
| (0.164) | |||
| essround9 × econ_satModerate | -0.185 | ||
| (0.115) | |||
| essround10 × econ_satModerate | -0.104 | ||
| (0.130) | |||
| essround11 × econ_satModerate | -0.013 | ||
| (0.155) | |||
| essround9 × econ_satLow | -0.063 | ||
| (0.140) | |||
| essround10 × econ_satLow | -0.024 | ||
| (0.147) | |||
| essround11 × econ_satLow | 0.073 | ||
| (0.168) | |||
| essround9 × immig_supportModerate | -0.045 | -0.055 | |
| (0.104) | (0.100) | ||
| essround10 × immig_supportModerate | -0.184+ | -0.231* | |
| (0.104) | (0.100) | ||
| essround11 × immig_supportModerate | -0.178+ | -0.188+ | |
| (0.105) | (0.102) | ||
| essround9 × immig_supportLow | 0.149 | 0.155 | |
| (0.136) | (0.131) | ||
| essround10 × immig_supportLow | 0.156 | 0.119 | |
| (0.150) | (0.145) | ||
| essround11 × immig_supportLow | -0.142 | -0.130 | |
| (0.147) | (0.142) | ||
| essround9 × country_attachModerate | -0.138 | -0.156 | |
| (0.112) | (0.110) | ||
| essround10 × country_attachModerate | -0.053 | -0.050 | |
| (0.112) | (0.110) | ||
| essround11 × country_attachModerate | -0.015 | -0.030 | |
| (0.114) | (0.112) | ||
| essround9 × country_attachLow | 0.128 | 0.125 | |
| (0.151) | (0.147) | ||
| essround10 × country_attachLow | 0.429** | 0.465** | |
| (0.150) | (0.146) | ||
| essround11 × country_attachLow | 0.507*** | 0.514*** | |
| (0.152) | (0.148) | ||
| essround9 × post_polonlineYes | 0.065 | ||
| (0.106) | |||
| essround10 × post_polonlineYes | 0.234* | ||
| (0.105) | |||
| essround11 × post_polonlineYes | 0.081 | ||
| (0.110) | |||
| Num.Obs. | 6467 | 6467 | 6467 |
| AIC | 42717.7 | 42600.8 | 42553.6 |
| BIC | 42853.2 | 43014.0 | 42851.7 |
We see in the full interaction model that many variables are not increasing over time.
Not significant differences over time: _ left right, gender
Some differences over time: - age groups, immigration support, country attachment.
In the regularised model, we are testing the slopes and see these differences between groups: Baseline = R8 value for all. SIGNIFICANT VALUES: AGE: Baseline = <20 trend * essround10:age_rec30-44 -> + 80.52% * essround10:age_rec>=65 -> + 82.61% * essround11:age_rec>=65 -> + 72.08%
IMMIG: Baseline = Support High * essround10:immig_supportModerate -> -20.61%
COUNTRY ATTACHMENT: Baseline = High * essround10:country_attachLow -> + 59.19% * essround11:country_attachLow -> + 67.21%
Summary table of outputs - for report
Create summary tables for appendix of the 4 ratio models (exlclude full model but make it available in the excel output files.
modelsummary(
list("Null model" = model_null_ratio,
"Time model" = model_time_ratio,
"Regularised main effects" = model_reg_ratio,
"Regularised interaction terms" = model_regint_ratio2),
metrics = c("AIC", "BIC", "RMSE", "R2"), # R2 might be pseudo R2 for GLM
gof_map = c("nobs", "aic", "bic"), # You can customize which GOF statistics to show
stars = TRUE, # Add significance stars
title = "Comparison of GLM Models with Weights"
)| Null model | Time model | Regularised main effects | Regularised interaction terms | |
|---|---|---|---|---|
| + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001 | ||||
| (Intercept) | -0.253*** | -0.440*** | -0.069 | 0.067 |
| (0.017) | (0.034) | (0.098) | (0.150) | |
| essround9 | 0.046 | 0.012 | 0.057 | |
| (0.049) | (0.045) | (0.199) | ||
| essround10 | 0.286*** | 0.132** | -0.238 | |
| (0.048) | (0.046) | (0.198) | ||
| essround11 | 0.432*** | 0.222*** | -0.086 | |
| (0.050) | (0.049) | (0.207) | ||
| genderFemale | 0.207*** | 0.203*** | ||
| (0.032) | (0.032) | |||
| age_rec20-29 | -0.089 | -0.201 | ||
| (0.080) | (0.150) | |||
| age_rec30-44 | -0.142+ | -0.367** | ||
| (0.075) | (0.143) | |||
| age_rec45-64 | -0.304*** | -0.409** | ||
| (0.075) | (0.141) | |||
| age_rec>=65 | -0.459*** | -0.741*** | ||
| (0.080) | (0.149) | |||
| left_rightModerate (4-6) | -0.208*** | -0.210*** | ||
| (0.042) | (0.042) | |||
| left_rightRight (7-10) | -0.575*** | -0.573*** | ||
| (0.056) | (0.056) | |||
| econ_satModerate | 0.055 | 0.063 | ||
| (0.047) | (0.047) | |||
| econ_satLow | 0.355*** | 0.356*** | ||
| (0.052) | (0.052) | |||
| immig_supportModerate | -0.349*** | -0.239*** | ||
| (0.037) | (0.071) | |||
| immig_supportLow | -0.677*** | -0.707*** | ||
| (0.052) | (0.095) | |||
| daily_netuseYes | 0.059 | 0.057 | ||
| (0.043) | (0.043) | |||
| country_attachModerate | 0.161*** | 0.223** | ||
| (0.040) | (0.078) | |||
| country_attachLow | 0.508*** | 0.234* | ||
| (0.052) | (0.108) | |||
| post_polonlineYes | 0.088* | 0.089* | ||
| (0.038) | (0.039) | |||
| essround9 × age_rec20-29 | 0.057 | |||
| (0.219) | ||||
| essround10 × age_rec20-29 | 0.369+ | |||
| (0.218) | ||||
| essround11 × age_rec20-29 | 0.022 | |||
| (0.227) | ||||
| essround9 × age_rec30-44 | 0.001 | |||
| (0.206) | ||||
| essround10 × age_rec30-44 | 0.591** | |||
| (0.206) | ||||
| essround11 × age_rec30-44 | 0.388+ | |||
| (0.214) | ||||
| essround9 × age_rec45-64 | -0.089 | |||
| (0.203) | ||||
| essround10 × age_rec45-64 | 0.215 | |||
| (0.204) | ||||
| essround11 × age_rec45-64 | 0.376+ | |||
| (0.213) | ||||
| essround9 × age_rec>=65 | 0.026 | |||
| (0.214) | ||||
| essround10 × age_rec>=65 | 0.602** | |||
| (0.212) | ||||
| essround11 × age_rec>=65 | 0.542* | |||
| (0.221) | ||||
| essround9 × immig_supportModerate | -0.055 | |||
| (0.100) | ||||
| essround10 × immig_supportModerate | -0.231* | |||
| (0.100) | ||||
| essround11 × immig_supportModerate | -0.188+ | |||
| (0.102) | ||||
| essround9 × immig_supportLow | 0.155 | |||
| (0.131) | ||||
| essround10 × immig_supportLow | 0.119 | |||
| (0.145) | ||||
| essround11 × immig_supportLow | -0.130 | |||
| (0.142) | ||||
| essround9 × country_attachModerate | -0.156 | |||
| (0.110) | ||||
| essround10 × country_attachModerate | -0.050 | |||
| (0.110) | ||||
| essround11 × country_attachModerate | -0.030 | |||
| (0.112) | ||||
| essround9 × country_attachLow | 0.125 | |||
| (0.147) | ||||
| essround10 × country_attachLow | 0.465** | |||
| (0.146) | ||||
| essround11 × country_attachLow | 0.514*** | |||
| (0.148) | ||||
| Num.Obs. | 6467 | 6467 | 6467 | 6467 |
| AIC | 44087.2 | 43979.2 | 42717.7 | 42553.6 |
| BIC | 44100.7 | 44013.1 | 42853.2 | 42851.7 |
Plotting regression model outputs
Plot main effects and interaction term marginal effects
Plot main effects with both ggeffects and sjPlot, model = model_regint_ratio
jtools::plot_summs(model_reg_ratio, scale=TRUE, exp = TRUE,
point.alpha = 0.7)+
labs(x="Multiplicative Change in Trust Ratio (trustEU/trustUK)")+
scale_x_continuous(breaks = seq(0.5, 2, 0.5),
limits = c(0.4, 2.1))
Plot of changes - for report
modelplot(model_reg_ratio,
exponentiate = TRUE, # This exponentiates the coefficients
conf_level = 0.95,
coef_map = c(
"essround9" = "ESS Round 9",
"essround10" = "ESS Round 10",
"essround11" = "ESS Round 11",
"genderFemale" = "Female",
"age_rec20-29" = "Age 20-29",
"age_rec30-44" = "Age 30-44",
"age_rec45-64" = "Age 45-64",
"age_rec>=65" = "Age 65+",
"left_rightModerate (4-6)" = "Moderate political views",
"left_rightRight (7-10)" = "Right leaning politically",
"econ_satModerate" = "Moderate economic satisfaction",
"econ_satLow" = "Low economic satisfaction",
"immig_supportModerate" = "Moderate immigration support",
"immig_supportLow" = "Low immigration support",
"daily_netuseYes" = "Daily internet user (Yes)",
"country_attachModerate" = "Moderate UK attachment",
"country_attachLow" = "Low UK attachment",
"post_polonline2" = "Posted political content online"
)) +
aes(color = (conf.low > 1 | conf.high < 1)) +
scale_color_manual(values = c("TRUE" = "#D55E00", "FALSE" = "grey50")) +
geom_vline(xintercept = 1, linetype = "dashed") +
labs(
x = "Multiplicative Change in Trust Ratio (trustEU/trustUK)",
color = "Significant at 5% level?" # Legend title
) +
theme_minimal()+
theme(legend.position="bottom")+
scale_x_continuous(breaks = seq(0.5, 2, 0.5),
limits = c(0.4, 2.1))
If we don’t use the interaction model, the predicted values are always
going to be equal between groups. We need to use the interaction term
model to view the changes over time.
sjPlot::plot_model(model_reg_ratio,
type = "pred",
terms = c("essround", "left_right"),
# terms = c("essround", "gender", "age_rec", "left_right",
# "econ_sat", "immig_support", "daily_netuse", "country_attach",
# "post_polonline"),
transform = "exp")+
theme_minimal()
## plotting age group marginal effects over time
plot_age <- sjPlot::plot_model(model_regint_ratio2,
type = "pred",
terms = c("essround", "age_rec"),
transform = "exp",
dot.size = 4,
line.size = 1)+
theme_minimal()+
geom_hline(yintercept = 0)+
labs(title = "Predicted trust ratio by age group, over time",
y = "Predicted trust ratio",
x = "ESS round",
colour = "Age group")+
geom_line(linewidth=2, alpha=0.5)+
geom_line(linewidth=2, alpha=0.5)+lims(y=c(-1, 1.3))## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.## plotting immigration attitudes
plot_immig <- sjPlot::plot_model(model_regint_ratio2,
type = "pred",
terms = c("essround", "immig_support"),
transform = "exp",
dot.size = 4,
line.size = 1)+
theme_minimal()+
geom_hline(yintercept = 0)+
labs(title = "Predicted trust ratio by immigration support level, over time",
y = "Predicted trust ratio",
x = "ESS round",
colour = "Immigration support:")+
geom_line(linewidth=2, alpha=0.5)+lims(y=c(-1.3, 0.75))## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.## plotting country attachment
plot_cntry <- sjPlot::plot_model(model_regint_ratio2,
type = "pred",
terms = c("essround", "country_attach"),
transform = "exp",
dot.size = 4,
line.size = 1)+
theme_minimal()+
geom_hline(yintercept = 0)+
labs(title = "Predicted trust ratio by country attachment level, over time",
y = "Predicted trust ratio",
x = "ESS round",
colour = "Attachment to UK:")+
geom_line(linewidth=2, alpha=0.5)+lims(y=c(-1, 1.3))## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.
Write output tables
Using jtools outputs instead of performancesummary. Prefer the overall summary statistics from this package although it does run slower. Uncomment if you want to save xlsx outputs.
# export the ratio model table to excel.
# jtools::export_summs(model_null_ratio,model_time_ratio, model_reg_ratio, model_regint_ratio2, model_full_ratio,
# to.file = "xlsx", file.name = "ratio_model_outputs.xlsx")
#
# jtools::export_summs(model_null_raw, model_time_raw, model_reg_raw, model_regint_raw2, model_full_raw,
# to.file = "xlsx", file.name = "raw_diff_model_outputs.xlsx")
# Additional testing
The analysis from here is additional and not included in any reports or findings.
Modelling time (essround) as a continuous varible
NOTE: THIS METHOD IS NOT PREFERRED. It was trialled but there is too much volatility is political trust and too few time periods to test for linearity.
Treating time as continuous will allow us to see the linear trends of the interactions as time is not set to the baseline but additive. first we relabel the time variable so that essround becomes time with Round 8 = 0 and R9=1,R10=2, R11=3.
# numeric values embedded in ESSround = essround8 = 1, R9=2... So we just subtract 1 to each value.
final_data_regress |>
mutate(time = as.numeric(essround)) |>
group_by(time, essround) |> count()Regularised model with raw target and continuous time variable:
## Regularised model WITH interactions
model_regintc_raw <- glm(target_diff ~ time + gender + age_rec + left_right*time + econ_sat +
immig_support*time + country_attach*time + post_polonline,
data = final_data_regress,
weights = anweight,
family = "gaussian")
summary(model_regintc_raw)##
## Call:
## glm(formula = target_diff ~ time + gender + age_rec + left_right *
## time + econ_sat + immig_support * time + country_attach *
## time + post_polonline, family = "gaussian", data = final_data_regress,
## weights = anweight)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.42426 0.17889 -2.372 0.01774 *
## time 0.35175 0.06228 5.647 1.70e-08 ***
## genderFemale 0.30896 0.05579 5.538 3.19e-08 ***
## age_rec20-29 -0.35237 0.13755 -2.562 0.01044 *
## age_rec30-44 -0.38502 0.12988 -2.964 0.00304 **
## age_rec45-64 -0.78625 0.12931 -6.080 1.27e-09 ***
## age_rec>=65 -1.04077 0.13518 -7.699 1.58e-14 ***
## left_rightModerate (4-6) -0.53053 0.12335 -4.301 1.72e-05 ***
## left_rightRight (7-10) -1.49876 0.16142 -9.285 < 2e-16 ***
## econ_satModerate 0.46823 0.08052 5.815 6.35e-09 ***
## econ_satLow 1.19351 0.09045 13.196 < 2e-16 ***
## immig_supportModerate -0.39853 0.10461 -3.810 0.00014 ***
## immig_supportLow -0.70469 0.13734 -5.131 2.97e-07 ***
## country_attachModerate 0.57788 0.11187 5.166 2.47e-07 ***
## country_attachLow 0.81808 0.15220 5.375 7.93e-08 ***
## post_polonlineYes 0.26536 0.06569 4.039 5.42e-05 ***
## time:left_rightModerate (4-6) -0.06988 0.06412 -1.090 0.27578
## time:left_rightRight (7-10) 0.01559 0.08502 0.183 0.85455
## time:immig_supportModerate -0.19286 0.05632 -3.424 0.00062 ***
## time:immig_supportLow -0.16343 0.07734 -2.113 0.03462 *
## time:country_attachModerate -0.10965 0.05970 -1.837 0.06631 .
## time:country_attachLow 0.19599 0.07940 2.469 0.01359 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 15.5827)
##
## Null deviance: 131347 on 6466 degrees of freedom
## Residual deviance: 100430 on 6445 degrees of freedom
## AIC: 29895
##
## Number of Fisher Scoring iterations: 2We see that with the time value as a continuous variable, we see these variables are significant: time:immig_supportModerate (-0.19) time:immig_supportLow (est = -0.16) time:country_attachLow (est = +0.19) –> Baseline group = R8 High